CN110334539A - A kind of personalized method for secret protection and device based on random response - Google Patents

A kind of personalized method for secret protection and device based on random response Download PDF

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CN110334539A
CN110334539A CN201910507049.9A CN201910507049A CN110334539A CN 110334539 A CN110334539 A CN 110334539A CN 201910507049 A CN201910507049 A CN 201910507049A CN 110334539 A CN110334539 A CN 110334539A
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value
sensitivity value
sensitivity
weight
target
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CN110334539B (en
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罗涛
宋海娜
李剑峰
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Beijing University of Posts and Telecommunications
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Beijing University of Posts and Telecommunications
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
    • G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
    • G06F21/6245—Protecting personal data, e.g. for financial or medical purposes

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Abstract

The embodiment of the invention provides a kind of personalized method for secret protection and device based on random response; wherein; the method is applied to client; it include: to obtain user to treat the target susceptibility value that collection target private data is inputted; the target private data corresponds to multiple sensitivity values, and the target susceptibility value is the sensitivity value in the multiple sensitivity value；Using preset personalized random response model, the target susceptibility value is disturbed, the sensitivity value after being disturbed；Wherein, the model is the introduction of the response model of the weight of each sensitivity value about target private data, and the model makes: probability of the probability of the sensitivity value output true value high to weight lower than the sensitivity value output true value low to weight.By this programme can solve it is existing based on the relevant technologies of random response privacy collect during, to certain private data overprotections, to the problem of certain private data guard deficiencies.

Description

A kind of personalized method for secret protection and device based on random response
Technical field
The present invention relates to private data guard technical fields, more particularly to a kind of personalized privacy based on random response Guard method and device.
Background technique
Data be in actual life by the reflection of respondent's concrete condition.In order to carry out various investigation, investigate Personnel (data collector) are usually by modes such as questionnaire surveys to by respondent (data set provider/participant/interviewee) It is putd question to, survey data required for obtaining.However, being usually contained the personal letter of extreme sensitivity in the answer of respondent Breath, data collector, which directly collects these data, may result in serious individual privacy leakage.
In the prior art, Warner is put forward for the first time in nineteen sixty-five utilizes random response (Randomized Response, RR) The collection of technology (hereinafter referred to as W-RR) progress privacy-sensitive data.Localization Privacy Preservation Mechanism based on RR model be intended to by Privacy leakage is controlled in source, while paying close attention to the quality of statistical data.Classical way in the technological borrowing statistical research, mould Quasi- investigator is collected into the process of valuable statistical data under the premise of not invading interviewee's privacy as far as possible.Random response skill Main current turbulence mechanism of the art as the localization secret protection technology based on data distortion, model simple is intuitive and is easy to real It is existing, and its level of disruption can directly quantify, the function admirable in terms of the estimation of statistical property, therefore receive extensive pass Note.
Since RR protects the privacy of data set provider by the way of answering according to probability, guarantee that tender subject is answered and had Very strong falsifiability has Privacy Safeguarding, in the secret protection tool and Apple System of Google Chrome Middle application.Meanwhile RR has fully considered a possibility that data collector steals or reveals privacy of user in data-gathering process, it should Interviewee independently can carry out privacy processing to individual data items in model, even data collector can not also obtain definitely Original private data, excite significantly interviewee participate in data collection enthusiasm.Therefore, it is protected different from the privacy of centralization Protection mechanism is for trusted third party it is assumed that the localization Privacy Preservation Mechanism based on RR no longer needs Jie of trusted third party Enter, while also eliminating the possible privacy leakage of insincere third party data collector and privacy attack.
Based on the W-RR model of above-mentioned classics, it is modeled usually using discrete memoryless binary channel, and is had Scholar is generalized to the symmetrical privacy information source of Multivariate Discrete, successively proposes the Privacy Preservation Mechanisms such as K-RR, O-RR, and has been obtained wide General application.
Difference privacy in 2006 is suggested as a kind of generally acknowledged strong secret protection model, receives many scholars' Favor.This development trend is complied with, many scholars have made intensive studies the localization difference privacy mechanism under RR model, should The secret protection degree of mechanism is measured in research by single privacy budget parameters ε, is concentrated mainly on difference optimal under RR model The design and analysis for dividing privacy mechanism pays close attention to the privacy-effectiveness compromise of whole system objectively.
It is above routine random response model (abbreviation CRR model, Conventional Randomized Response) and In its related application and research, concern is primarily with the objective secret protection degree of whole system.The model defaults different privacies Data are of equal importance, and secret protection demand is identical, and then are carried out identical privacy preserving operations to all private datas.
However, there are great differences for the subjective feeling (i.e. susceptibility) due to data set provider to private data, and privacy The susceptibility of data reflects the demand height of secret protection to a certain extent；Therefore, the secret protection of data set provider needs It asks and is not quite similar, varies with each individual.In other words, in the collection of actual private data, private data is not of equal importance.For this Kind of situation, existing CRR model default that different private datas are of equal importance, secret protection demand is identical, this is likely to result in certain A little private data overprotections, and the shortcoming protection of certain private datas, it is especially excessive to the private data of certain low sensitivities Protection is short of protection to the private data of certain high sensitives.
Summary of the invention
The embodiment of the present invention is designed to provide a kind of personalized method for secret protection and device based on random response, To solve the prior art during privacy is collected, to certain private data overprotections, not to certain private data guards The problem of foot.Specific technical solution is as follows:
In a first aspect, the embodiment of the invention provides a kind of, the personalized method for secret protection based on random response is applied to Client, which comprises
It obtains user and treats the target susceptibility value that collection target private data is inputted, the target private data correspondence is more A sensitivity value, the target susceptibility value are the sensitivity value in the multiple sensitivity value；
Using preset personalized random response model, the target susceptibility value is disturbed, it is quick after being disturbed Inductance value；Wherein, the personalized random response model is the introduction of the weight of each sensitivity value about target private data Response model, and the personalized random response model makes: the probability of the sensitivity value output true value high to weight lower than pair The probability of the low sensitivity value output true value of weight；Wherein, the weight of the high sensitivity value of secret protection demand is higher than secret protection The weight of the low sensitivity value of demand.
Optionally, the expression formula of the personalized random response model are as follows:
Wherein, xiFor sensitivity value to be disturbed；
M is the number of each sensitivity value about target private data, m >=2；
wiFor the sensitivity value xiWeight；
Pi,j(wi) it is about the weight wiSubtraction function；
yjFor the sensitivity value after the corresponding disturbance of the sensitivity value wait disturb.
Optionally, in the personalized random response model,
Wherein, μiFor the adjustment parameter of preset secret protection degree.
Optionally, described using preset personalized random response model, the target susceptibility value is disturbed, is obtained Sensitivity value after disturbance, comprising:
According to P=Pi,j(wi) probability to the target susceptibility value respond output true value, according to's Probability responds output disturbance value to the target susceptibility value, the sensitivity value after being disturbed.
Optionally, in the personalized random response model,And 0 < wi<m。
Second aspect, the personalized privacy protection device based on random response that the embodiment of the invention also provides a kind of, is answered For client, described device includes:
Module is obtained, treats the target susceptibility value collecting target private data and being inputted, the target for obtaining user Private data corresponds to multiple sensitivity values, and the target susceptibility value is the sensitivity value in the multiple sensitivity value；
Module is disturbed, for disturbing, obtaining to the target susceptibility value using preset personalized random response model Sensitivity value after to disturbance；Wherein, the personalized random response model is the introduction of about each quick of target private data The response model of the weight of inductance value, and the personalized random response model makes: the sensitivity value output true value high to weight Probability lower than low to weight sensitivity value output true value probability；Wherein, the weight of the high sensitivity value of secret protection demand The weight of the sensitivity value low higher than secret protection demand.
Optionally, the expression formula of the personalized random response model are as follows:
Wherein, xiFor sensitivity value to be disturbed；
M is the number of each sensitivity value about target private data, m >=2；
wiFor the sensitivity value xiWeight；
Pi,j(wi) it is about the weight wiSubtraction function；
yjFor the sensitivity value after the corresponding disturbance of the sensitivity value wait disturb.
Optionally, in the personalized random response model,
Wherein, μiFor the adjustment parameter of preset secret protection degree.
Optionally, the disturbance module is specifically used for according to P=Pi,j(wi) probability the target susceptibility value is responded True value is exported, according toProbability to the target susceptibility value respond output disturbance value, it is quick after being disturbed Inductance value.
Optionally, in the personalized random response model,And 0 < wi<m。
The third aspect, the embodiment of the invention also provides a kind of electronic equipment, including processor, communication interface, memory And communication bus, wherein processor, communication interface, memory complete mutual communication by communication bus；
Memory, for storing computer program；
Processor when for executing the program stored on memory, realizes method and step described in above-mentioned first aspect.
Fourth aspect, it is described computer-readable to deposit the embodiment of the invention also provides a kind of computer readable storage medium It is stored with computer program in storage media, when the computer program is executed by processor, realizes described in above-mentioned first aspect Method and step.
The difference of present invention method prominent high sensitive privacy and low sensitivity privacy, fully considers different sensitivities It is worth personalized secret protection demand, introduces sensitivity value weight, and be introduced into the decision of random response, based on random During the localization data of response is collected, data set provider can carry out the hidden of personalization to private data according to the privacy requirements of oneself Privates reason: carrying out high-intensitive random perturbation to the sensitive Value Data of high sensitive, and the data of low sensitivity carry out lower strong The random perturbation of degree；Respective desired secret protection degree can be reached by ensuring different sensitivity value groups, realize individual character The secret protection of change, this method can solve the prior art during privacy is collected, right to certain private data overprotections The problem of certain private data guard deficiencies.
In addition, present invention method is capable of providing the higher noisy data of availability, has excellent statistical property. Under conditions of the subjective privacy leakage degree of mechanism is certain, the personalized random response model that is used in the present embodiment method The quality for the statistical data that (abbreviation PRR model, Personalized Randomized Response) is collected into is compared to CRR Model is higher, that is, the statistical estimate error of PRR model is smaller, and the accuracy of statistical estimate is higher, is one kind towards statistics With the practical approach of analysis, there is stronger practical significance.The noisy data that data collector is collected by statistics with analysis, just The distribution situation of sensitive value information can be obtained, that is, obtain effective statistical estimate result.
In addition, the embodiment of the present invention can not only be to high sensitive in the survey based on polynary random response model The sensitivity value of degree carries out sufficiently high secret protection, and carries out relatively small secret protection to the sensitivity value of low sensitivity and (reach To its privacy requirements)；And the privacy leakage degree (or secret protection grade) of each sensitivity value can intuitively be fed back Interviewee (local end user) is given, interviewee is made to reacquire certain power control data (such as access right, limitation disposal right), The enthusiasm of interviewee is excited, excitation user more objectively submits the data after the disturbance of random response mechanism, therefore has Hope the trouble and worry for fundamentally solving data set provider, so be collected into compared to CRR model it is more objective, it is quality, available Property higher (distribution of privacy information estimation accuracy is higher) data.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with It obtains other drawings based on these drawings.
Fig. 1 is a kind of process of the personalized method for secret protection based on random response provided by the embodiment of the present invention Figure；
Fig. 2 shows for a kind of structure of the personalized privacy protection device based on random response provided by the embodiment of the present invention It is intended to；
Fig. 3 is the structural schematic diagram of a kind of electronic equipment provided by the embodiment of the present invention；
Fig. 4 is the schematic diagram that the localization privacy based on random response model collects process；
Fig. 5 is the objective privacy leakage degree figure of each sensitivity value under 3-CRR model；
Fig. 6 is the subjective privacy leakage degree figure of each sensitivity value under 3-PRR model；
Fig. 7 is the schematic diagram of the evaluated error of two kinds of models of 3-CRR and 3-PRR.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other Embodiment shall fall within the protection scope of the present invention.
In order to solve the prior art during privacy is collected, to certain private data overprotections, to certain privacy numbers According to the problem that protection is insufficient, the embodiment of the present invention provides a kind of personalized method for secret protection and device based on random response.
The personalized method for secret protection based on random response is provided for the embodiments of the invention first below to be situated between It continues.
Before introducing the method for the present invention, the application scenarios for collecting privacy information are briefly explained first.
When carrying out privacy collection, user logs in client, and the display interface of client is shown and the target to be collected The related problem of private data and various options, user submit after selecting for problem option as answer, wherein each Kind option is multiple sensitivity values of target private data, and the target susceptibility value that the answer is chosen by user.Client obtains The answer that user submits, and the disturbance based on random response is carried out to the answer, the answer after being disturbed, i.e. noisy data. Data analyst obtains noisy data of a large number of users about target private data, by a large amount of noisy datas and individual character being collected into It is for statistical analysis to change random response model, the distribution situation of sensitive value information can be obtained, that is, obtain effective statistical estimate As a result.It should be noted that the disturbance focused on to target susceptibility value of the invention, realizes the target susceptibility of high privacy requirements Value executes the disturbance of higher-strength, and the target susceptibility value of low privacy requirements executes more low intensive disturbance, to realize personalization Secret protection.As for by disturbance after a large amount of noisy datas it is for statistical analysis, by being analyzed in a large amount of noisy data The statistical property of its group belongs to the prior art out, is not focus of the invention.
It is understood that method provided by the embodiment of the present invention is that the answer submitted to user of client is disturbed Dynamic method.And provided method can solve the prior art during privacy is collected through the embodiment of the present invention, to certain A little private data overprotections, to the problem of certain private data guard deficiencies.
As shown in Figure 1, the present embodiment provides a kind of personalized method for secret protection based on random response, is applied to client End, the method may include:
S101 obtains user and treats the target susceptibility value that collection target private data is inputted, the target private data Corresponding multiple sensitivity values, the target susceptibility value are the sensitivity value in the multiple sensitivity value；
It should be noted that target private data to be collected refers to that the individual privacy of user, privacy can be by questionnaire tune A problem in looking into discloses.For example, for " you suffer from AIDS? " the problem of, the answer "Yes" or "No" of acquisition is The individual privacy of user is contained.
User couple and the possible answer of target private data relevant issues, the as corresponding sensitivity value of target private data, The quantity of sensitivity value is at least two.Target susceptibility value is some sensitivity value in multiple sensitivity values, refers to that user asks in answer The practical answer made when topic, the quantity for single user its target susceptibility value is one.
Still with " you suffer from AIDS? " the problem of for, "Yes" or "No" is target private data corresponding two A sensitivity value；If user answers "Yes", then "Yes" is target susceptibility value.It is, of course, understood that target susceptibility value is not It is confined to "Yes" or "No".For example, when " are you with AIDS, cancer, which kind of disease in influenza? " is proposed for target group The problem of when, it is " AIDS ", " cancer " and " influenza " respectively, and target that the corresponding sensitivity value of target private data, which is three, Sensitivity value, the i.e. answer of user then can be " AIDS ", " cancer ", one in " influenza ".
S102 disturbs the target susceptibility value, after obtaining disturbance using preset personalized random response model Sensitivity value；
Wherein, the personalized random response model is the introduction of the weight of each sensitivity value about target private data Response model, and the personalized random response model makes: the probability of the sensitivity value output true value high to weight is lower than The probability of the sensitivity value output true value low to weight；Wherein, the weight of the high sensitivity value of secret protection demand is protected higher than privacy The weight of the low sensitivity value of shield demand.
Illustratively, in one implementation, the expression formula of personalized random response model can be with are as follows:
Wherein, xiFor sensitivity value to be disturbed；
M is the number of each sensitivity value about target private data, m >=2；
wiFor the sensitivity value xiWeight；
Pi,j(wi) it is about the weight wiSubtraction function；
yjFor the sensitivity value after the corresponding disturbance of the sensitivity value wait disturb.
For sensitivity value to be disturbed, user prompts according to the display interface of client, inputs some sensitivity to be disturbed Value is used as target susceptibility value xi, the target susceptibility value x of client acquisition user's submissioni, to target susceptibility value xiIt is disturbed.Such as Fruit target susceptibility value xiWeight wiGreatly, then output true value is responded to the target susceptibility value with lesser probability, that is, carried out high The random perturbation of intensity；If target susceptibility value xiWeight wiIt is small, then the target susceptibility value is responded with biggish probability defeated True value out carries out more low intensive random perturbation；Sensitivity value y after finally obtaining disturbancej。
Based on the expression formula of above-mentioned personalized random response model, the specific implementation of step S102 may include: According to P=Pi,j(wi) probability to the target susceptibility value respond output true value, according toProbability to institute State target susceptibility value response output disturbance value, the sensitivity value after being disturbed.
It should be noted that the weight of the sensitivity value of private data is big in personalization random response model of the embodiment of the present invention The height of the small secret protection demand for reflecting individual: the weight of sensitivity value is bigger, and the demand of secret protection is higher.Weight is set Surely follow two principles: the susceptibility of principle one, sensitivity value is higher, and the weight of corresponding sensitivity value is bigger；Principle two, sensitivity value Frequency it is lower, corresponding sensitivity value weight is bigger.The weight of sensitivity value and specific application scenarios, individual subjective feeling, The factors such as individual preference, economic benefit are related.In addition, the setting of weight is generally by proposing ask related with target private data The problem of topic distribution platform or the comprehensive each factor of data analyst determine, naturally it is also possible to open permission is customized by the user.
The difference of present invention method prominent high sensitive privacy and low sensitivity privacy, fully considers different sensitivities It is worth personalized secret protection demand, introduces sensitivity value weight, and be introduced into the decision of random response, based on random During the localization data of response is collected, data set provider can carry out the hidden of personalization to private data according to the privacy requirements of oneself Privates reason: carrying out high-intensitive random perturbation to the sensitive Value Data of high sensitive, and the data of low sensitivity carry out lower strong The random perturbation of degree；Respective desired secret protection degree can be reached by ensuring different sensitivity value groups, realize individual character The secret protection of change, the method overcome CRR models to be carried out muting sensitive sense brought by identical operation to all sensitivity values The sensitivity value overprotection of degree, and the insufficient disadvantage of sensitive value protection of high sensitive can solve the prior art and receive in privacy During collection, to certain private data overprotections, to the problem of certain private data guard deficiencies.
In order to meet sensitivity value weight wiBigger, secret protection demand is higher, and tendency response exports the probability of its true value Relatively low trend, in one implementation, in personalized random response model:
Wherein, μiFor the adjustment parameter of preset secret protection degree, and μi≥0。
It should be noted that can be according to sensitivity value xiParameter μ is arranged in the specific secret protection demand of groupi.In practice may be used To pass through adjustment parameter μiTo realize personalized secret protection, while the subjective privacy leakage degree of Controlling model: privacy is protected Shield demand is higher, it is contemplated that lesser μ is arrangediValue, vice versa.
Optionally, in personalized random response model in the above-described embodiment,And 0 < wi<m.It can With understanding, sensitivity value weight wiMeetAnd 0 < wiWhen < m, i.e. satisfaction " returning mization " condition, it is based on PRR model All theory deductions and analysis be suitable for CRR model, it can compatible CRR model.Stated differently, since CRR model is simultaneously The sensitivity for not considering each sensitivity value is considered as a kind of equal situation of sensitivity value weight, i.e., to institute in PRR model Some sensitivity value xi, sensitivity value weight is wi≡ 1 (i=1,2 ..., m), after returning mization to handle, CRR model is PRR mould A kind of special case of type.
As shown in Fig. 2, corresponding to above method embodiment, the embodiment of the invention also provides a kind of based on random response Personalized privacy protection device, is applied to client, and described device includes:
Module 201 is obtained, treats the target susceptibility value collecting target private data and being inputted, the mesh for obtaining user Mark private data corresponds to multiple sensitivity values, and the target susceptibility value is the sensitivity value in the multiple sensitivity value；
Module 202 is disturbed, for being disturbed to the target susceptibility value using preset personalized random response model It is dynamic, the sensitivity value after being disturbed；Wherein, the personalized random response model is the introduction of about each of target private data The response model of the weight of a sensitivity value, and the personalized random response model makes: the sensitivity value output high to weight is true Probability of the probability of real value lower than the sensitivity value output true value low to weight；Wherein, secret protection demand high sensitivity value Weight is higher than the weight of the low sensitivity value of secret protection demand.
Optionally, the expression formula of the personalized random response model are as follows:
Wherein, xiFor sensitivity value to be disturbed；
M is the number of each sensitivity value about target private data, m >=2；
wiFor the sensitivity value xiWeight；
Pi,j(wi) it is about the weight wiSubtraction function；
yjFor the sensitivity value after the corresponding disturbance of the sensitivity value wait disturb.
Optionally, in the personalized random response model,
Wherein, μiFor the adjustment parameter of preset secret protection degree.
Optionally, the disturbance module 202 is specifically used for according to P=Pi,j(wi) probability to the target susceptibility value ring True value should be exported, according toProbability to the target susceptibility value respond output disturbance value, after being disturbed Sensitivity value.
Optionally, in the personalized random response model,And 0 < wi<m。
As shown in figure 3, additionally providing a kind of electronic equipment, including processor in another embodiment provided by the invention 301, communication interface 302, memory 303 and communication bus 304, wherein processor 301, communication interface 302, memory 303 are logical It crosses communication bus 304 and completes mutual communication；
Memory 303, for storing computer program；
Processor 301 when for executing the program stored on memory, realizes that the embodiment of the present invention is based on random response Personalized method for secret protection.
In another embodiment provided by the invention, a kind of computer readable storage medium, the computer are additionally provided It is stored with computer program in readable storage medium storing program for executing, when the computer program is executed by processor, realizes the embodiment of the present invention Personalized method for secret protection based on random response.
Acquired beneficial effect in order to further illustrate the present invention, below by the PRR model of the invention used and now There is the CRR model in technology to compare, the design and analysis of PRR model of the present invention itself is described in further detail.
For CRR model in the prior art, default privacy data be it is of equal importance, then different sensitivity values is hidden Private protection demand is identical.The secret protection model for setting CRR model is PCRR, it can be indicated by the row stochastic matrix of m × m:
Identical privacy preserving operations are carried out to every kind of sensitivity value --- it is (i.e. true with the probability output true value of P Sensitivity value), one of other m-1 kind sensitivity values are exported with the probability respondence of (1-P)/(m-1), which is referred to as M-CRR model, wherein PCRRFor the symmetrical matrix of m × m, and meet | PCRR| ≠ 0, i.e. PCRRThere are invertible matrix.
The parameter designing of K-RR model is used for reference, parameter lambda >=0 is introduced, in order to meet all sensitivity value xiSecret protection need It asks, while requiring the privacy leakage risk of data minimum, to all sensitivity value xiWithProbability Response output true value, substitutes into structural formula (1), then PCRRSecret protection model expression formula are as follows:
In practice, lambda parameter can be according to sensitivity value xiSpecific secret protection demand is configured.Given sensitivity value weight wi1, secret protection demand is bigger, then corresponding λ should be arranged smaller.
And in personalized random response model of the invention, for sensitivity value xi, subjective sensitivity is higher, corresponding quick Inductance value weight wiBigger, the demand to secret protection is also higher.PRR model considers personalized secret protection demand, will be sensitive It is worth weight wiIt is introduced into sensitivity value xiIn the decision of random response, then the secret protection model P of PRR modelPRRIt is described as
The PRR model is referred to as m-PRR model.Wherein, Pi,i(wi) can vividly describe to possess sensitivity value xiIndividual The probability told the truth.By formula (3) it is found that sensitivity value x under m-PRR modeliThe probability for exporting true value is Pi,i(wi), and Pi,i (wi) it is about wiSubtraction function.This is because working as Pi,i(wi) when meeting some requirements, sensitivity value x is reflected from sidei Secret protection degree: Pi,i(wi) smaller, sensitivity value xiSecret protection degree it is higher, corresponding privacy leakage risk is smaller, Vice versa.
For sensitivity value xi, according to sensitivity value weight wiWith secret protection model PPRRRelationship, to all sensitivity value xiIt is (quick Inductance value group) withProbability respondence its true value, meet simultaneously: Pi1,i1≤Pi2,i2≤...≤ Pim,im, substitute into structural formula (2), then PPRRThe expression formula of secret protection model be
Wherein, μiIt can be according to sensitivity value xiThe specific secret protection demand of group is configured, and mainly overcomes CRR mould Type is carried out this problem of identical privacy preserving operations for all sensitivity values, realizes the personalization towards more sensitivity values Secret protection.For sensitivity value xiFor, sensitivity value weight wiIt is bigger, it is meant that secret protection demand is higher, tendency response output The probability of its true value is relatively low.
The embodiment of the present invention passes through personalized random response model in view of the susceptibility of different sensitivity values is different, to privacy The sensitivity value x of dataiPersonalized random perturbation is carried out, realizes personalized secret protection；To the sensitivity value number of high sensitive According to the random perturbation for carrying out high intensity, and more low intensive random perturbation is carried out to the data of low sensitivity, that is, susceptibility is high Sensitivity value individual, with lower probability output true value；And the sensitivity value that susceptibility is low, with higher probability output true value； It is mutually indepedent between each individual；Obtain noisy data.The noisy data that data collector is collected by statistics with analysis, The distribution situation of sensitive value information is obtained, that is, obtains effective statistical estimate result.
Due to weighting maximum a posteriori probability (PMAP) criterion according to privacy, the subjective privacy leakage of PRR model can be obtained Degree Lcw, each sensitivity value subjective privacy leakage degree Lcwi, so that the privacy for measuring whole system and each sensitivity value is protected Therefore shield degree is defined as follows first.
Define 1PMAP criterion:
Select reconstruction of functionIt is allowed to meet condition
Wherein,Indicate weighting posteriority transition probability wiPX|Y(xi|yj) weight of target susceptibility value is corresponded to when taking maximum, Sensitivity value y after indicating disturbancejSensitive value information after being denoised by reconstruction of function.Above-mentioned criterion is known as " privacy weighting maximum Posterior probability (PMAP) criterion " or " privacy weighting minimum total error probability criterion ".
Define 2 privacies weighting accuracy/Bayes's effectiveness:
Pass through weight wiUnder conditions of (i=1,2 ..., m) portrays privacy scheme taker acquisition posterior information, using PMAP standard Then, it enables
It then can correctly deduce the probability-weighted with subjective feeling privacy information
Claim the probability PcwAccuracy is weighted for privacy or privacy weights Bayes's effectiveness, wherein PY(yj) indicate that disturbance is defeated Sensitivity value y outjProbability.PcwModel subjective data safety: P can be measuredcwBigger, the subjective safety of model is lower. In addition, PcwVisual interpretation are as follows: in primary conjecture, consider that sensitivity value weight bring influences, benefit weighted most according to privacy Bigization principle, the correct maximum weighted probability guessed (or reasoning) and go out to participate in the true privacy information of individual.
Define 3 subjective privacy leakage degree:
The subjective privacy leakage degree of PRR model be defined as privacy weighting accuracy under the conditions of known posterior information with only The ratio of privacy weighting accuracy under the conditions of known prior information, is described as
Wherein,Lcw>=1 reflection is to consider that sensitivity value weight bring influences, PMAP criterion Under by secret protection model bring with respect to privacy leakage degree, value is bigger, and corresponding privacy leakage degree is higher.
For sensitivity value xiGroup for, that focus more on is sensitivity value xiPrivacy leakage degree or secret protection Degree, and the subjective secret protection degree of not entire secret protection system.For PRR model, sensitivity value is under PMAP criterion xiAnd correctly deduce xiPrivacy probability-weighted be
Wherein,It indicates to Hamming distance inversion operation, ifThenOtherwise Pcw(xi) meet:
Define 4 sensitivity value xiSubjective privacy leakage degree:
Under PRR model, sensitivity value xiSubjective privacy leakage degree LcwiBeing defined as sensitivity value under PMAP criterion is xiAnd just Really deduce xiPrivacy probability-weighted and sensitivity value xiPrivacy weighting prior probability ratio, be described as
Wherein, PX(xi) indicate sensitivity value x to be collectediProbability, LcwiMeet: 0≤Lcwi≤1。LcwiIt is bigger, illustrate sensitivity Value xiSubjective privacy leakage degree is higher, it is meant that PRR model is to sensitivity value xiDegree of protection is lower.In practice, it is contemplated that The susceptibility of each sensitivity value is different, the sensitivity value high for susceptibility, and subjective privacy leakage degree should be relatively small, such as Higher secret protection degree may be implemented in this；And the sensitivity value low for susceptibility, subjective privacy leakage degree should can be with It is relatively large, because its demand to secret protection degree is lower.
For ease of description, following simplification: P is done to the notation of partial symbolsX(xi) it is abbreviated as Pi(i ∈ 1,2 ... m }), CRR and PRR model is denoted as P respectivelyCRR、PPRR.Sensitivity value weight is arranged according to sequence from big to small: wi1≥wi2≥...≥ wim, the secret protection demand of corresponding sensitivity value is sequentially reduced, according to sensitivity value weight wiWith the relationship (formula of channel transition probability (2)), should meet by the probability that RR model exports its true value accordingly: Pi1,i1≤Pi2,i2≤...≤Pim,im, wherein quick Inductance value weight wi1Corresponding sensitivity value xi1Group's secret protection demand is maximum, and by PRR model, its secret protection degree also should It is maximum.
According to sensitivity value weight wiWith secret protection model PPRRRelationship (formula (2)), each sensitivity value by PRR model ring The probability that its true value should be exported meets: Pi1,i1≤Pi2,i2≤...≤Pim,im.At this point, P under note PRR modeliExperience estimation Value is denoted asPrior distribution PXExperience estimation be denoted asBy PXPPRR=PY, in statistical estimate model, give PPRR, and | PPRR| ≠ 0, thenAlso it can be described as:
Wherein,For PPRRInverse matrix.
The assessment thought for using for reference the privacy profile estimation performance of K-RR model, usesTo assess PRR model Privacy profile estimation problem, referred to as evaluated error: evaluated error is smaller, and the accuracy of privacy profile estimation is higher, i.e. data Availability is higher.
For any m, N and Pi,i≠ 1/m (i=1,2 ..., m, N > > m), sensitive value information distribution estimates under PRR model Meter error can be described as:
Wherein,Expression pairIts desired value is sought after carrying out two norm operations,Indicate PRR Model lower channel transition probability inverse matrixThe i-th row, jth column element,Indicate PRR Output data Y under modelNMiddle yjShared actual ratio,Indicate posterior probabilityDesired value.
Due to that can not deriveAnalytic solutions, formula (12) only providesThe expression formula of accuracy in the case of numerical solution. In practice, PPRRIt is usually known, i.e. its inverse matrixBe it is known, therefore formula (12) be can be obtained by calculation.
Under CRR model, different sensitivity values are of equal importance, then the privacy requirements of each sensitivity value are identical.At this point, to each sensitivity Value is performed both by identical privacy preserving operations, and the probability that response exports its true sensitivity value meets:
Pi1,i1=Pi2,i2=...=Pim,im=P.
In view of sensitivity value xi1Sensitivity value weight wi1Maximum, the demand highest of secret protection, response output true value Probability Pi1,i1=P is minimum.
Remember P under CRR modeliExperience estimated value be denoted asPrior distribution PXExperience estimation be denoted asThen its The evaluated error of privacy profile can be described as:
Smaller, i.e., evaluated error is smaller, illustrates that the accuracy of privacy profile estimation is higher.
The privacy profile estimated value of PRR modelPrivacy profile estimated value relative to CRR modelEfficiency be defined as Relative efficiency Re:
Under certain subjective privacy leakage degree, the availability of data is maximized, that is, maximizes the statistical quality of data, Relative efficiency ReMeet: Re≥1.This is because CRR model all uses identical privacy preserving operations to all sensitivity values, it is Meet respective privacy requirements while minimizing privacy leakage degree, generally uses secret protection demand maximum sensitivity value institute The secret protection parameter setting needed so will cause the low sensitivity value group overprotection of other susceptibilitys.And PRR model is Its privacy parameters is set according to the personalized privacy requirements of specific sensitivity value, realizes personalized secret protection.On the other hand, Demand of the sensitivity value group of low sensitivity to secret protection is lower, and the probability that tendency response exports its true value is relatively large, So biggish contribution is made for the availability of statistical data.Obviously, the PRR model towards more sensitivity values that the present invention is mentioned Estimated valueIt is more efficient compared to CRR model.
In order to make it easy to understand, being emulated below with reference to example by MATLAB, simulation investigator is come using random response model It carries out data collection and then estimates the process of privacy information distribution.
The prior distribution of privacy information source is PX=[P1,P2,...,Pm], the sample size of participant is N (N > > m), sample X1,X2,...,Xn,...,XN(n=1,2 ..., N) come from distribution PX, between Different Individual private data it is mutually indepedent and with point Cloth, and assume that each individual only possesses a kind of sensitivity value.
As shown in figure 4, the sample of participant passes through RR model PY|XRandom perturbation processing (treat the sensitivity value of disturbance Disturbed, the sensitivity value after obtaining corresponding disturbance), to obtain noisy data.In Fig. 4, the sample of participant is X1, X2,...,Xn,...,XN(n=1,2 ..., N), sensitivity value to be disturbed are x1,…xi,…xm(i=1,2 ..., m), correspondence are disturbed Sensitivity value after dynamic is y1,…yj,…ym(j=1,2 ..., m), the sample (i.e. noisy data) after disturbance are Y1,…Yn,…YN (n=1,2 ..., N).
When emulation, setting participates in sample number N=105, sample value set X={ x1,x2,x3}={ AIDS, Cancer, Flu }, i.e. m=3, every kind of disease represents a kind of sensitivity value, and samples sources are in the prior distribution P of privacy information sourceX=[P1,P2,P3] =[0.2,0.3,0.5] (note: prior distribution P in practiceXIt is unknown, and m=3 is set herein, PX=[0.2,0.3,0.5] An only special case, the PRR model that the present invention is mentioned are suitable for the individual character based on RR under any m >=2 yuan Discrete Finite sensitivity value Change secret protection).
The secret protection model of formula (2) and formula (4) is respectively adopted in CRR model and PRR model, and wherein m=3, is denoted as 3-CRR Model and 3-PRR model.
For the ease of analysis, μ is set in PRR modeli=μj=μ (i, j=1,2,3).
Wherein, according to sensitivity value weight wiSetting principle, three kinds of sensitivity value weights are as shown in table 1.
As shown in Table 1, sensitivity value={ AIDS, cancer, flu }, sensitivity value weight successively reduces, i.e. sensitivity value x1It is right " AIDS " the susceptibility highest answered, the maximum of sensitivity value weight setting；And sensitivity value x3Corresponding " Flu " susceptibility is minimum, The setting of its sensitivity value weight is minimum.
1 three kinds of sensitivity value weights of table and its prior probability distribution
Sensitivity value X x1 x2 x3
Disease type AIDS Cancer Flu
Sensitivity value weight wi(×3) 0.6 0.3 0.1
Probability 0.2 0.3 0.5
In PRR model, the high sensitivity value individual of susceptibility, with lower probability output true value, and low quick of susceptibility Inductance value is mutually indepedent between each individual with higher probability output true value.As shown in table 1, individual is submitting sensitive data When, possess the individual of " AIDS " with 20% probability output true value, is in other two kinds of sensitivity values with 40% probability transfer Any one；Similarly, possess the individual of " cancer " with the output true value of 30% probability, exported with 35% probability respondence For any one in other two kinds of sensitivity values；Possess the individual of " flu " with the output true value of 50% probability, with 25% Probability respondence output be other two kinds of sensitivity values in any one.
Fig. 5 gives the objective privacy leakage degree L of 3-CRR model each sensitivity value under CMAP criterionciWith parameter lambda Change curve.
As shown in Figure 5, as λ≤1.65 (critical value is related with specific prior distribution), privacy leakage degree is by low X is followed successively by height1、x2、x3, show the highest sensitivity value x of susceptibility1Secret protection degree highest, substantially to sensitivity value x1It is real Complete protection is showed.
But as λ > 1.65, x1、x2、x3Subjective privacy leakage degree LciCurve co-insides illustrate CRR model at this time Lower three kinds of sensitivity values are the same in secret protection degree objectively.In practice, x3Corresponding " Flu " group is perhaps to hidden The demand of private protection is not high, and x1Demand of corresponding " AIDS " group to secret protection is relatively high, so causes low The sensitivity value x of susceptibility3Overprotection, and the sensitivity value x of high sensitive1Shortcoming protection.
Therefore, under certain conditions, CRR model is identical to the secret protection degree of all sensitivity values, this be its not Foot place.
Fig. 6 gives the subjective privacy leakage degree L of 3-PRR model each sensitivity value under PMAP criterioncwiWith parameter μ Change curve.
It will be appreciated from fig. 6 that the subjective privacy leakage degree of three sensitivity values increases with the increase of parameter μ.
As one timing of parameter μ, the sensitivity value x being sequentially reduced for susceptibility1、x2、x3(be corresponding in turn to AIDS, Cancer, Flu), subjective secret protection leak degree is sequentially increased, it is meant that sensitivity value x1、x2、x3Subjective secret protection degree successively Increase, this is because the susceptibility of its sensitivity value is lower, corresponding secret protection demand is lower.In other words, PRR model is to quick The highest sensitivity value x of sensitivity1Secret protection degree highest, the sensitivity value x minimum to susceptibility3Secret protection degree it is opposite Minimum, this is also the PRR model of the invention mentioned compared to where the advantage of CRR model.
It is better than CRR model in terms of statistical quality to verify the PRR model that the present invention is mentioned, needs in identical condition Under be compared and analyze, the subjective privacy leakage degree (define 3) of emulation setting of the embodiment of the present invention under two kinds of models Centainly.
Fig. 7 provides the simulation value (i.e. calculated value) of the evaluated error of 3-CRR model and 3-PRR model privacy profile, and gives The theoretical value of evaluated error is used as with reference to (Monte Carlo simulation is set as 10 out4It is secondary).
As shown in Figure 7, the simulation value and calculated value of privacy profile evaluated error are almost the same under two kinds of models, verifying The above-mentioned correctness for quality of data theory analysis.
In addition, as subjective privacy leakage degree LcwOne timing, the evaluated error of CRR model privacy profile are above PRR mould The accuracy of type, i.e. PRR modeling statistics estimation is higher, and performance is more preferably.
On the other hand, under two kinds of models the evaluated error of privacy profile with subjective privacy leakage degree LcwIncrease and Reduce.But in survey and analysis, it is generally desirable to its subjective privacy leakage degree is the smaller the better but such by interviewee The availability of data that will cause statistical analysis reduces, and illustrates it is conflicting between availability of data and privacy.In practice, it answers The accuracy and secret protection demand according to specific privacy profile carry out good compromise.
Table 2 illustrates the respectively required parameter setting and privacy profile of two kinds of models under five kinds of subjective privacy leakage degree The relative efficiency R of estimationeSimulation value (formula (14)), and provide theoretical value be used as with reference to (Monte Carlo simulation setting 104It is secondary).
As shown in Table 2, when the subjective privacy leakage degree of two kinds of models is identical, relative efficiency ReCalculated value and Simulation calculation value is almost the same, and (conclusion of this and Fig. 7 are consistent to the correctness and validity for demonstrating theory analysis of the present invention ).
The parameter setting and its relative efficiency R of two kinds of models under conditions of the subjective privacy leakage degree of table 2 is certaineEmulation Value and theoretical value
On the other hand, relative efficiency ReIt is all satisfied: Re>=1, it is meant that when private data is not of equal importance, consider individual character The privacy requirements of change, under the premise of subjective privacy leakage degree is certain, PRR model is better than in terms of statistical data availability CRR model, and PRR model can be directed to different sensitivity value flexible modulation privacy parameters μ, realize personalized secret protection, This above-mentioned theory analysis result is consistent.I.e. under certain subjective privacy leakage, PRR model can be hidden according to each sensitivity value The height of private demand realizes the personalized secret protection data collection based on random response.
Furthermore the subjective privacy leakage degree of two kinds of models increases with the increase of parameter lambda and μ, at this time privacy number According to safety constantly reducing.Therefore, it needs that suitable λ and μ value is arranged according to specific secret protection demand in practice, in turn By privacy leakage extent control in reasonable range, realization carries out good compromise between Information Security and statistical quality.
In short, CRR model is enough to solve the survey of problems and analysis if private data is of equal importance.But Data in practice are not of equal importance, and secret protection demand is also not quite similar.Compared to CRR model, in the present embodiment method The PRR model of use is by introducing sensitivity value weight, it is contemplated that the personalized privacy requirements of different sensitivity values overcome CRR mould In type all sensitivity values are carried out with the operation of identical secret protection, it can be ensured that different sensitivity value groups is attained by one periodically The secret protection degree of prestige, and compared to the sensitivity value of low sensitivity, the secret protection degree of the sensitivity value of high sensitive is higher, Personalized secret protection is realized, there is important practical significance.
It should be noted that, in this document, relational terms such as first and second and the like are used merely to a reality Body or operation are distinguished with another entity or operation, are deposited without necessarily requiring or implying between these entities or operation In any actual relationship or order or sequence.Moreover, the terms "include", "comprise" or its any other variant are intended to Non-exclusive inclusion, so that the process, method, article or equipment including a series of elements is not only wanted including those Element, but also including other elements that are not explicitly listed, or further include for this process, method, article or equipment Intrinsic element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that There is also other identical elements in process, method, article or equipment including the element.
Each embodiment in this specification is all made of relevant mode and describes, same and similar portion between each embodiment Dividing may refer to each other, and each embodiment focuses on the differences from other embodiments.Especially for system reality For applying example, since it is substantially similar to the method embodiment, so being described relatively simple, related place is referring to embodiment of the method Part explanation.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the scope of the present invention.It is all Any modification, equivalent replacement, improvement and so within the spirit and principles in the present invention, are all contained in protection scope of the present invention It is interior.

Claims (10)

1. a kind of personalized method for secret protection based on random response is applied to client, which is characterized in that the method packet It includes:
It obtains user and treats the target susceptibility value that collection target private data is inputted, the target private data corresponds to multiple quick Inductance value, the target susceptibility value are the sensitivity value in the multiple sensitivity value；
Using preset personalized random response model, the target susceptibility value is disturbed, the sensitivity value after being disturbed； Wherein, the personalized random response model is the introduction of the response mould of the weight of each sensitivity value about target private data Type, and the personalized random response model makes: the probability of the sensitivity value output true value high to weight is lower than low to weight Sensitivity value output true value probability；Wherein, it is low to be higher than secret protection demand for the weight of the high sensitivity value of secret protection demand Sensitivity value weight.
2. the method according to claim 1, wherein
The expression formula of the personalization random response model are as follows:
Wherein, xiFor sensitivity value to be disturbed；
M is the number of each sensitivity value about target private data, m >=2；
wiFor the sensitivity value xiWeight；
Pi,j(wi) it is about the weight wiSubtraction function；
yjFor the sensitivity value after the corresponding disturbance of the sensitivity value wait disturb.
3. according to the method described in claim 2, it is characterized in that, in the personalization random response model,
Wherein, μiFor the adjustment parameter of preset secret protection degree.
4. according to the method in claim 2 or 3, which is characterized in that it is described to utilize preset personalized random response model, The target susceptibility value is disturbed, the sensitivity value after being disturbed, comprising:
According to P=Pi,j(wi) probability to the target susceptibility value respond output true value, according toProbability Output disturbance value is responded to the target susceptibility value, the sensitivity value after being disturbed.
5. according to the method in claim 2 or 3, which is characterized in that in the personalization random response model,And 0 < wi<m。
6. a kind of personalized privacy protection device based on random response is applied to client, which is characterized in that described device packet It includes:
Module is obtained, treats the target susceptibility value collecting target private data and being inputted, the target privacy for obtaining user Data correspond to multiple sensitivity values, and the target susceptibility value is the sensitivity value in the multiple sensitivity value；
Module is disturbed, for disturbing, being disturbed to the target susceptibility value using preset personalized random response model Sensitivity value after dynamic；Wherein, the personalized random response model is the introduction of each sensitivity value about target private data Weight response model, and the personalized random response model makes: the sensitivity value output true value high to weight it is general Probability of the rate lower than the sensitivity value output true value low to weight；Wherein, the weight of the high sensitivity value of secret protection demand is higher than The weight of the low sensitivity value of secret protection demand.
7. device according to claim 6, which is characterized in that
The expression formula of the personalization random response model are as follows:
Wherein, xiFor sensitivity value to be disturbed；
M is the number of each sensitivity value about target private data, m >=2；
wiFor the sensitivity value xiWeight；
Pi,j(wi) it is about the weight wiSubtraction function；
yjFor the sensitivity value after the corresponding disturbance of the sensitivity value wait disturb.
8. device according to claim 7, which is characterized in that in the personalization random response model,
Wherein, μiFor the adjustment parameter of preset secret protection degree.
9. device according to claim 7 or 8, which is characterized in that
The disturbance module is specifically used for according to P=Pi,j(wi) probability to the target susceptibility value respond output true value, press According toProbability output disturbance value, sensitivity value after being disturbed are responded to the target susceptibility value.
10. device according to claim 7 or 8, which is characterized in that in the personalization random response model,And 0 < wi<m。
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