CN112182645A - Quantifiable privacy protection method, equipment and medium for destination prediction - Google Patents

Quantifiable privacy protection method, equipment and medium for destination prediction Download PDF

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CN112182645A
CN112182645A CN202010967393.9A CN202010967393A CN112182645A CN 112182645 A CN112182645 A CN 112182645A CN 202010967393 A CN202010967393 A CN 202010967393A CN 112182645 A CN112182645 A CN 112182645A
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蒋洪波
王孟源
肖竹
刘代波
曾凡仔
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Abstract

The invention discloses a method, equipment and a medium for quantifiable privacy protection aiming at destination prediction, wherein the method comprises the following steps: injecting noises with different radiuses into a plurality of groups of same historical track data sets by using a Laplace mechanism to obtain corresponding track data sets; performing destination prediction on each group of track data sets, calculating corresponding privacy protection degrees according to prediction results to further construct training samples, and taking the noise radius grade as a label value of the training samples; training a privacy quantification protection model based on multiple linear regression by using all training samples and label values thereof; when receiving a privacy protection degree requirement and a track to be protected, inputting the privacy protection degree requirement to a privacy quantitative protection model; and injecting noise into the track to be protected according to the noise radius level output by the privacy quantization protection model by utilizing a Laplace mechanism. The method and the device can fully meet the privacy requirements of the user and provide accurate and stable track privacy protection for the user.

Description

Quantifiable privacy protection method, equipment and medium for destination prediction
Technical Field
The invention belongs to the field of privacy protection and information security, and provides a quantifiable privacy protection method, equipment and a medium for destination prediction aiming at the position privacy threat of high-accuracy destination prediction service.
Background
With the popularization of mobile equipment with a GPS and the continuous development of the technology of the Internet of things, more and more services based on position location facilitate the aspects of our lives. Such as business activities, personal health management, etc. In recent years, a technique for predicting a user destination has been developed. The technology predicts the position which the mobile user wants to reach according to the partial track of the mobile device user and a prediction model established by a historical track database of a city. This technology has significantly pushed the development of location-based services, but also poses a significant privacy threat.
Applications using this technology are now emerging in many scenarios. For example, in automatic driving, destination prediction automatically generates a place where a passenger will go for the convenience of the passenger; in some social software, the technology predicts a place where a user wants to go according to a track traveled by the user so as to push an advertisement related to a user destination; at the drip research institute, drip companies utilize the technology to improve the riding experience of users and the order taking efficiency of drip drivers. The latest relevant research results of the drip research institute show that the prediction technology of the drip company can achieve extremely high prediction accuracy, and the average distance error between the prediction result and a real destination is within 1 km. The latest article shows that under the conditions of sufficient data volume and complete user travel track, the probability of successfully predicting the destination position of the user can reach 80%, and meanwhile, the average distance error can be limited within 500 meters.
High accuracy and precision bring better location-based application services to mobile end users, but at the same time pose a great threat to user privacy and personal safety. The concrete expression is as follows: (1) in past research, research on the trajectories of mobile devices has shown that destination locations are often associated with sensitive locations of users, such as the user's home, the user's place of work, a bar frequented by the user, etc., and thus exposure of sensitive locations is a serious violation of the user's personal privacy; (2) an attacker hostile to the user exists, if the accurate destination position of the user is predicted through the position positioning server, the attacker can go to the destination in advance to wait for the user and then attack the user at random, and the attacker is a great personal safety threat to the user; (3) nobody can guarantee that a server providing location-based services is absolutely authentic, and when the server grasps the trajectory of a user and predicts the accurate destination of the user, the destination of the user is at risk of leakage.
In the past, few methods for protecting the privacy of a user destination are used, and a common method is an end point removal method, namely, at a user mobile terminal, the head end and the tail end of a user track are removed, then the removed incomplete track is sent to a service provider, and the destination predicted by the service provider is not too accurate. However, this approach is too crude, provides extremely unstable privacy protection, and there are special cases (e.g., there are stops at the beginning and end) that defeat the protection method.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a method, an apparatus and a medium for quantifiable privacy protection for destination prediction in location services, which can fully meet the privacy requirements of users and provide users with accurate and stable track privacy protection.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
a quantifiable privacy preserving method for destination prediction, comprising:
step 1, acquiring training data;
obtaining a set of historical track data sets DtrainM groups of same historical track data sets are obtained through copying;
respectively injecting the radius r into the M groups of historical track data sets by using a Laplace mechanismi，i∈[0，1，...，M-1]To obtain a trajectory data set with different noise added:
Figure BDA0002682839910000021
ri＝r0+i×rbase，i∈[0，1，...，M-1]，
in the formula (I), the compound is shown in the specification,
Figure BDA0002682839910000022
representing the ith group of track data sets, wherein the injected noise radius level is i;
Figure BDA0002682839910000023
represents the jth track, L, in the ith set of historical track data setsDRepresenting the size of the historical track data set; r isiRepresenting the radius of noise, r, injected into the ith set of historical track data0To use the radius randomly generated by the Laplace mechanism, rbaseIs a preset noise base radius;
Figure BDA0002682839910000024
show to the track
Figure BDA0002682839910000025
Random angle of implantation and radius riThe noise of (2);
for L in each set of trace dataDPredicting the destination of each track, counting and calculating the privacy protection degree of each group of track data sets according to the prediction result, and then generating a plurality of derivative values according to the obtained privacy protection degree and a preset function; the privacy protection degree and the derivative value thereof correspondingly obtained by each group of track data sets form a group of training samples, and the noise radius grade i corresponding to each track data set is used as the label value of each training sample;
step 2, training a protection model;
training a privacy quantification protection model based on multiple linear regression by taking M groups of training samples as input and taking corresponding label values as output;
step 3, processing track noise;
when receiving a privacy protection degree requirement and a track to be protected, inputting the privacy protection degree requirement to a privacy quantization protection model, and outputting to obtain a noise radius grade to be injected into the track; the trajectory to be protected is compared with a historical trajectory data set DtrainThe tracks in (1) belong to the same area;
and injecting noise into the track to be protected according to the noise radius level output by the privacy quantization protection model by using a Laplace mechanism, and finally sending the track subjected to noise injection to a position server.
Further, the privacy quantification protection model based on multiple linear regression can be represented by substituting M sets of training samples and their corresponding label values into the following linear relationship sets:
Figure BDA0002682839910000031
in the formula, λ0，λ1，λ2，...，λqTo quantify the parameters of the protection model for privacy0，∈2，...，∈M-1To protect the bias value of the model, xi，1，xi，2，...，xi，qAre the ith set of training samples, yiFor the label value of the ith set of training samples, i ∈ [0,1]；
Training a multiple linear regression-based protection model, i.e. solving the parameter lambda corresponding to the minimum of the following optimization function0，λ1，λ2，...，λqThe optimization function is:
Figure BDA0002682839910000032
further, after a privacy quantization protection model is obtained through training by using a training sample, pruning simplification processing is further carried out on the obtained privacy quantization protection model; and 3, acquiring the Laplace noise radius grade to be injected into the track by using the simplified privacy quantization protection model.
Further, the pruning simplification treatment process comprises the following steps:
step A1, training the privacy quantification protection model obtained by using the training sample, and determining the parameter lambda of the privacy quantification protection model0，λ1，λ2，...，λqStored in a parameter sequence lambda*And a sequence of buffer parameters lambdatIts optimization function value is expressed as SSE*(ii) a Storing M groups of training samples in variable array A*And cache variable array At；
Step A2, for parameter sequence λ*The privacy quantization protection model is formed, and each variable is calculated based on a variable array A*Correlation coefficient R with labelkAll correlation coefficients R to be obtainedkIs stored in a list R*(ii) a Wherein the correlation coefficient RkThe calculation formula of (2) is as follows:
Figure BDA0002682839910000033
in the formula, xi，kIs a variable array A*K variable value, y of the ith training sample setiFor the label value corresponding to the ith set of training samples,
Figure BDA0002682839910000034
is a variable array A*The k-th variable mean of the middle M training samples,
Figure BDA0002682839910000035
the average value of M label values corresponding to M groups of training samples is obtained;
step A3, from List R*Selecting the minimum correlation coefficient, setting the subscript as s, and combining the minimum correlation coefficient RsAnd comparing with a preset correlation coefficient threshold value:
if the minimum correlation coefficient RsIf the correlation coefficient is larger than a preset correlation coefficient threshold value, the parameter sequence lambda is determined to be*The formed privacy quantification protection model is used as a final simplified privacy quantification protection model;
if the minimum correlation coefficient RsIf the correlation coefficient is less than or equal to the preset correlation coefficient threshold value, the parameter sequence lambda is cachedtParameter lambda with middle subscript ssSet to 0, buffer variable array AtVariable value slave cache variable array A with middle subscript stGet out of the buffer, and then buffer the variable array AtAnd sample label is substituted into a buffer parameter sequence lambdatThe formed privacy quantitative protection model calculates the optimization function value and stores the optimization function value in the SSEtComparison of SSEtAnd SSE*：
If SSEt＞SSE*Then the parameter sequence λ is maintained*Without change, will be represented by a parameter sequence λ*The formed privacy quantification protection model is used as a final simplified privacy quantification protection model;
if SSEt≤SSE*Then the parameter sequence is lambda*Updating to and caching parameter sequence lambdatSame, variable array A*Update to and cache variable array AtSame, SSE*Is replaced with SSEtAnd returning to the step A2.
Further, a specific method for calculating the privacy protection degree of each group of track data sets according to the prediction result comprises the following steps:
let i group trajectory data set
Figure BDA0002682839910000041
Middle LDL corresponding to the strip trackDThe prediction accuracy of the G partitions distributed in the track is P from large to smalli，1，Pi，2，...，Pi，GTogether forming an ith set of trajectory data sets
Figure BDA0002682839910000042
To a sequence of accuracies
Figure BDA0002682839910000043
Then according to the accuracy sequence
Figure BDA0002682839910000044
Calculating the privacy protection degree WAE of each group of track data setsi：
Figure BDA0002682839910000045
Wherein, P'gFor historical track data set DtrainPrediction accuracy at the g-th partition.
Further, the preset function used for generating the derivative value according to the privacy protection degree includes any one or more of the following: y is x2，y＝x3，y＝x-1，y＝x-2，
Figure BDA0002682839910000046
y is log x, y is ln x; wherein x is the privacy protection degree and y is the derived value.
Further, a predetermined noise base radius rbase＝500m。
The invention also provides an apparatus comprising a processor and a memory; wherein: the memory is to store computer instructions; the processor is configured to execute the computer instructions stored in the memory, and in particular, to perform any of the methods described above.
The present invention also provides a computer storage medium storing a program for implementing any of the above methods when executed.
Advantageous effects
Before a user track is sent to a position server from a mobile terminal, a privacy quantitative protection model established by multiple linear regression is utilized, and the corresponding level of noise adding processing can be obtained according to the privacy protection degree requirement of the user on the track; then, by utilizing a Laplace privacy mechanism in the differential privacy and according to the noise radius grade obtained by the privacy quantitative protection model, quantitative noise adding processing is carried out on the track to be protected of the user, the privacy requirement of the user can be fully met, and accurate and stable track privacy protection can be provided for the user. In addition, the invention adopts a pruning simplified processing method to remove redundant parameters in the privacy quantitative protection model, thereby reducing the calculated amount of the privacy quantitative protection model and improving the processing speed of the privacy protection method.
Drawings
FIG. 1 is a technical circuit diagram of a method according to an embodiment of the invention;
FIG. 2 is a MAE of the accuracy of privacy protection provided under different privacy requirements based on a Bohr diagram and Beijing taxi track data sets in an experiment according to an embodiment of the present invention;
FIG. 3 is a graph of stability RMSE of privacy protection provided under different privacy requirements based on Bohr diagram and Beijing taxi track data sets in an experiment according to an embodiment of the present invention;
FIG. 4 shows the successful protection ratio at different user satisfaction thresholds in an experiment according to an embodiment of the present invention;
FIG. 5 is a graph of the effect of differential privacy parameters on RMSE providing privacy protection based on a Bohr-chart taxi track dataset, obtained experimentally in accordance with an embodiment of the present invention;
fig. 6 shows the influence of differential privacy parameters on RMSE for privacy protection provided based on a beijing taxi track dataset, obtained through experiments according to an embodiment of the present invention.
Detailed Description
The following describes embodiments of the present invention in detail, which are developed based on the technical solutions of the present invention, and give detailed implementation manners and specific operation procedures to further explain the technical solutions of the present invention.
The embodiment of the invention provides a quantifiable privacy protection method for destination prediction, which comprises the following steps:
step 1, acquiring training data;
(1) obtaining a set of historical track data sets DtrainM groups of same historical track data sets are obtained through copying;
(2) respectively injecting the radius r into the M groups of historical track data sets by using a Laplace mechanismi，i∈[0，1，...，M-1]To obtain a trajectory data set with different noise added:
Figure BDA0002682839910000051
ri＝r0+i×rbase，i∈[0，1，...，M-1]，
in the formula (I), the compound is shown in the specification,
Figure BDA0002682839910000052
representing the ith group of track data sets, wherein the injected noise radius level is i;
Figure BDA0002682839910000053
represents the jth track, L, in the ith set of historical track data setsDRepresenting the size of the historical track data set; r isiRepresenting the radius of noise, r, injected into the ith set of historical track data0To use the radius randomly generated by the Laplace mechanism, rbaseIn this embodiment, r is taken for a predetermined noise base radiusbase＝500m；
Figure BDA0002682839910000061
Show to the track
Figure BDA0002682839910000062
Random angle of implantation and radius riThe noise of (2);
historical track data set DtrainEach track in (a) may be denoted as T ═ l1，l2，...，lcWherein l isiIn this embodiment, according to a laplacian mechanism in differential privacy, noise is injected into each position point in the track to obtain a false position point deviating from an original position point, so that all false position points form a false track containing noise, and the false track is sent to the server end instead of the original real track, thereby achieving the purpose of privacy protection on the real track.
Wherein the radius r is randomly generated by using a Laplace mechanism0The generation method comprises the following steps:
according to the laplacian mechanism, the probability service density formula of the false location points:
Figure BDA0002682839910000063
wherein r is represented as a false position point and a real position point liAnd theta represents the degree of an included angle between a connecting line of the false position point and the real position point and a horizontal coordinate axis in the rectangular coordinate system. And r and theta are random variables independent of each other, so by integration we obtain the set of probability density equations for r and theta:
Figure BDA0002682839910000064
Figure BDA0002682839910000065
from the formula, the angles corresponding to the false positions can be randomly generated through uniform distribution.
The cumulative distribution function due to the distance r is:
Figure BDA0002682839910000066
thus, a final laplace distance r generating function can be obtained:
Figure BDA0002682839910000067
wherein, the epsilon represents the privacy sensitivity of the Laplace distance r, and under the same condition, the smaller the epsilon, the better the privacy protection effect; w-1Is a Lambert W function method, p is the random probability of laplace brazilian fabry, and e is a natural constant.
From the above formula
Figure BDA0002682839910000068
The obtained laplace distance r is the radius r randomly generated by using the laplace mechanism in this embodiment0。
(3) For L in each set of trace dataDThe method comprises the following steps of predicting destinations of tracks, and calculating the privacy protection degree of each group of track data sets according to the prediction result, and comprises the following specific steps:
a1, setting the ith group track data set
Figure BDA0002682839910000069
Middle LDL corresponding to the strip trackDThe prediction accuracy of the G partitions distributed in the track is P from large to smalli，1，Pi，2，...，Pi，GTogether forming an ith set of trajectory data sets
Figure BDA00026828399100000610
To a sequence of accuracies
Figure BDA00026828399100000611
Let the map on which the trajectories in the M sets of trajectory data sets are distributed be divided into G cells. Thus, for L in each set of trace data setsDThe strip track is predicted, and L can be obtainedDAnd (6) predicting the result. Wherein, each prediction result has G prediction probabilities, corresponding to G partitions. Ranking of a possible destination can be obtained according to the arrangement of the prediction probabilities from large to small. Thus, this rank has G ranks. According to the prediction probability, if the partition position corresponding to one bit in the ranking is the real destination of the user, the position prediction is accurate once. Thus passing through LDPredicting the track, and counting the times of successful prediction of each rank in the accumulated ranking to obtain a list Ni，p1，Ni，p2，...，Ni，pG. Then, according to this list, based on
Figure BDA0002682839910000071
The prediction accuracy of each group of track data sets at each bit can be calculatedAccuracy Pi，g，i∈[0，1，...，M-1]，g∈[1，2，...，G]。
a2, then according to the accuracy sequence
Figure BDA0002682839910000072
Calculating the privacy protection degree WAE of each group of track data setsi：
Figure BDA0002682839910000073
Wherein, Pi，gAnd P'gIndicating the accuracy of the ranking at the location of list g after privacy protection and before privacy protection, respectively.
Due to the accuracy of the sequence
Figure BDA0002682839910000074
The sections are arranged in the order from big to small, and the sections with higher rank are more easily attacked by attackers, so the invention hopes to reduce the accuracy of the positions with higher rank to ensure that the track privacy is fully protected, and the weighted absolute error, namely the privacy protection degree formula is selected to define the privacy protection degree. Since an attacker tends to select a prediction result with a ranked position with higher accuracy to attack, the higher the accuracy of the position with the higher ranking, the greater the influence, and the greater the assigned privacy weight. Therefore, here choose
Figure BDA0002682839910000075
The effect of variations in accuracy for distinguishing different ranked locations on the degree of privacy protection, such as assuming that the accuracy of the first and fifth ranked locations varies by Δ P1And Δ P5And Δ P1＝ΔP5From the formula, we know that the two coefficients are 1 and 1/5, respectively, then in the accumulation,
Figure BDA0002682839910000076
therefore, the judgment users of the attackers with different prediction accuracies are well distinguishedThe location of the real destination.
Figure BDA0002682839910000077
In (1),
Figure BDA0002682839910000078
the corresponding noise is 0, i.e. this is a true prediction without noise. Then, through the formula for calculating the WAE, we calculate M privacy protection degree values, which are respectively WAE0，WAE1，...，WAEM-1Then wherein WAE0＝0。
a3, and then generating a plurality of derivative values according to the obtained privacy protection degree according to the following preset functions: y is x2，y＝x3，y＝x-1，y＝x-2，
Figure BDA0002682839910000079
y is log x, y is ln x; wherein x is the privacy protection degree, and y is the derived value; therefore, a group of training samples can be formed by the privacy protection degree and the derivative value thereof correspondingly obtained by each group of track data sets, and the noise radius grade i corresponding to each track data set is used as the label value of each training sample;
step 2, training a protection model;
training a privacy quantification protection model based on multiple linear regression by taking M groups of training samples as input and taking corresponding label values as output; wherein, substituting M sets of training samples and their corresponding label values can be expressed as the following linear relationship set:
Figure BDA0002682839910000081
in the formula, λ0，λ1，λ2，...，λqTo quantify the parameters of the protection model for privacy0，∈2，...，∈M-1To protect the bias value of the model, xi，1，xi，2，…，xi，qAre respectively asIth set of training samples, yiFor the label value of the ith set of training samples, i ∈ [0,1]；
Training a multiple linear regression-based protection model, i.e. solving the parameter lambda corresponding to the minimum of the following optimization function0，λ1，λ2，...，λqThe optimization function is:
Figure BDA0002682839910000082
in this embodiment, the Scikit-left machine learning library is used to calculate λ0，λ1，...，λqThe optimization function value SSE is minimized.
After the privacy quantization protection model is obtained through training by using the training sample, pruning simplification processing is further carried out on the obtained privacy quantization protection model, so that the privacy quantization protection model is simplified on the basis of ensuring the privacy protection effect, and the calculated amount of privacy quantization is reduced. The pruning simplified treatment process comprises the following steps:
step A1, training the privacy quantification protection model obtained by using the training sample, and determining the parameter lambda of the privacy quantification protection model0，λ1，λ2，...，λqStored in a parameter sequence lambda*And a sequence of buffer parameters lambdatIts optimization function value is expressed as SSE*(ii) a Storing M groups of training samples in variable array A*And cache variable array At；
Step A2, for parameter sequence λ*The privacy quantization protection model is formed, and each variable is calculated based on a variable array A*Correlation coefficient R with labelkAll correlation coefficients R to be obtainedkIs stored in a list R*(ii) a Wherein the correlation coefficient RkThe calculation formula of (2) is as follows:
Figure BDA0002682839910000083
in the formula, xi，kIs a variable array A*K variable value, y of the ith training sample setiFor the label value corresponding to the ith set of training samples,
Figure BDA0002682839910000084
is a variable array A*The k-th variable mean of the middle M training samples,
Figure BDA0002682839910000085
the average value of M label values corresponding to M groups of training samples is obtained;
step A3, from List R*Selecting the minimum correlation coefficient, setting the subscript as s, and combining the minimum correlation coefficient RsAnd comparing with a preset correlation coefficient threshold value:
if the minimum correlation coefficient RsIf the correlation coefficient is larger than a preset correlation coefficient threshold value, the parameter sequence lambda is determined to be*The formed privacy quantification protection model is used as a final simplified privacy quantification protection model;
if the minimum correlation coefficient RsLess than or equal to the preset correlation coefficient threshold (set to 0.5 in this embodiment), the buffer parameter sequence λ is obtainedtParameter lambda with middle subscript ssSet to 0, buffer variable array AtVariable value slave cache variable array A with middle subscript stGet out of the buffer, and then buffer the variable array AtAnd sample label is substituted into a buffer parameter sequence lambdatThe formed privacy quantitative protection model calculates the optimization function value and stores the optimization function value in the SSEtComparison of SSEtAnd SSE*：
If SSEt＞SSE*Then the parameter sequence λ is maintained*Without change, will be represented by a parameter sequence λ*The formed privacy quantification protection model is used as a final simplified privacy quantification protection model;
if SSEt≤SSE*Then the parameter sequence is lambda*Updating to and caching parameter sequence lambdatSame, variable array A*Update to and cache variable array AtSame, SSE*Is replaced with SSEtAnd returning to the step A2.
Step 3, processing track noise;
as shown in fig. 1, when a privacy protection degree requirement (PR) and a track to be protected are received, the privacy protection degree requirement is input to a final simplified privacy quantization protection model of pruning simplification processing, and a radius level of noise to be injected into the track is output; the trajectory to be protected is compared with a historical trajectory data set DtrainThe tracks in (1) belong to the same area;
according to the noise radius grade output by the privacy quantization protection model, noise is injected into the track to be protected, the track after the noise is injected is sent to the position server instead of the original real track, the real track is protected according to the privacy protection degree requirement, and an attacker is prevented from carrying out destination prediction attack on the user through the track received by the server.
Experimental setup
In order to verify the effectiveness of different scenes and attack methods, the method provided by the invention is provided with two groups of data sets, wherein one group of data sets is from a T-Drive project and is a Beijing taxi data set collected from 9 month 1 to 10 month 25 in 2013. Another set of boldo taxi data from Kaggle collected for days 7, 1 in 2013 to 30 in 6, 2014.
There are three types of attack models: SubSyn and T-DesP are Markov transition matrix based destination prediction models, Distribution is Gaussian probability Distribution and trajectory clustering based prediction models, and H-TALL is a neural network based prediction model.
This experiment has set up three indexes, the reliability of the presentation effect:
Figure BDA0002682839910000091
Figure BDA0002682839910000092
Figure BDA0002682839910000093
MAE and RMSE are well able to represent the error, and stability, of the protection provided by our protection framework. And ER represents the degree of satisfaction of the user, and the number of times of prediction of satisfaction of the user is the ratio of the total number of times according to different threshold values omega.
The experimental contents have the following three aspects:
(1) effect against different attack methods (destination prediction methods);
(2) effects in different scenarios (datasets of different sizes);
(3) the influence of parameter setting on the effect;
setting parameters:
PR：0.1，0.2，0.3，0.4，0.5，0.6；
∈：0.1，0.3，0.5，0.7，0.9，1.1，1.3，1.5，1.7；
ω：0.01，0.02，0.03，0.04，0.05；
results of the experiment
Fig. 2 and 3 account for the situation where our protection model provides quantitative privacy protection on two data sets for four different attack methods based on the privacy requirements of the user. Fig. 2 and 3 illustrate the accuracy (MAE) and stability (RMSE), respectively, of quantitative privacy protection provided by our privacy protection framework for user privacy needs. First, in fig. 2, the protection average bias value provided by us is not more than 0.04 at the highest on different data sets and different attack methods. Moreover, the protection effect of the method exceeds 0.02 in the attack method of Distribution only when PR is equal to 0.2. That is, our privacy preserving framework is capable of providing extremely precise protection, i.e., with a maximum deviation of PR ± 0.2, in most cases. Fig. 3 also shows that the maximum root mean square error of our protection framework does not exceed 0.04, that is, the protection effect provided by our framework is very stable in multiple privacy protections.
Fig. 4 shows the relationship between the satisfaction threshold and the satisfaction degree. It can be seen that when the user has severe requirements (i.e. ω < 0.3), our protection can satisfy most users, even the best case, and the satisfaction can reach more than 90%. And when the requirements of users are more relaxed (namely omega is more than 0.3), the satisfaction degree of the protection effect can be extremely close to 100% under the condition of more than half.
Finally, the influence of the noise parameter e on the protection effect, here the relation between the RMSE and the noise parameter e is used. Fig. 5 and 6 plot the results of the experiment on different data sets. With the increase of noise parameters, the privacy protection effect of the user is weakened, so that the privacy is better protected by selecting a lower differential privacy parameter epsilon.
The above embodiments are preferred embodiments of the present application, and those skilled in the art can make various changes or modifications without departing from the general concept of the present application, and such changes or modifications should fall within the scope of the claims of the present application.

Claims (9)

1. A quantifiable privacy preserving method for destination prediction, comprising:
step 1, acquiring training data;
obtaining a set of historical track data sets DtrainM groups of same historical track data sets are obtained through copying;
respectively injecting the radius r into the M groups of historical track data sets by using a Laplace mechanismi,i∈[0,1,…,M-1]To obtain a trajectory data set with different noise added:
Figure FDA0002682839900000011
ri＝r0+i×rbase,i∈[0,1,…,M-1]，
in the formula (I), the compound is shown in the specification,
Figure FDA0002682839900000012
representing the ith group of track data sets, wherein the injected noise radius level is i;
Figure FDA0002682839900000013
represents the jth track, L, in the ith set of historical track data setsDRepresenting the size of the historical track data set; r isiRepresenting the radius of noise, r, injected into the ith set of historical track data0To use the radius randomly generated by the Laplace mechanism, rbaseIs a preset noise base radius;
Figure FDA0002682839900000014
show to the track
Figure FDA0002682839900000015
Random angle of implantation and radius riThe noise of (2);
for L in each set of trace dataDPredicting the destination of each track, counting and calculating the privacy protection degree of each group of track data sets according to the prediction result, and then generating a plurality of derivative values according to the obtained privacy protection degree and a preset function; the privacy protection degree and the derivative value thereof correspondingly obtained by each group of track data sets form a group of training samples, and the noise radius grade i corresponding to each track data set is used as the label value of each training sample;
step 2, training a protection model;
training a privacy quantification protection model based on multiple linear regression by taking M groups of training samples as input and taking corresponding label values as output;
step 3, processing track noise;
when receiving a privacy protection degree requirement and a track to be protected, inputting the privacy protection degree requirement to a privacy quantization protection model, and outputting to obtain a noise radius grade to be injected into the track; the trajectory to be protected is compared with a historical trajectory data set DtrainThe tracks in (1) belong to the same area;
and injecting noise into the track to be protected according to the noise radius level output by the privacy quantization protection model by using a Laplace mechanism, and finally sending the track subjected to noise injection to a position server.
2. The method of claim 1, wherein the multiple linear regression-based privacy quantification protection model substitutes M sets of training samples and their corresponding label values into a linear relationship set that can be expressed as:
Figure FDA0002682839900000016
in the formula, λ0,λ1,λ2,…,λqTo quantify the parameters of the protection model for privacy0,∈2,…,∈M-1To protect the bias value of the model, xi,1,xi,2,…,xi,qAre the ith set of training samples, yiFor the label value of the ith set of training samples, i ∈ [0,1, …, M-1]；
Training a multiple linear regression-based protection model, i.e. solving the parameter lambda corresponding to the minimum of the following optimization function0,λ1,λ2,…,λqThe optimization function is:
Figure FDA0002682839900000021
3. the method according to claim 2, characterized in that after the privacy quantitative protection model is obtained by training with the training sample, the obtained privacy quantitative protection model is further pruned and simplified; and 3, acquiring the Laplace noise radius grade to be injected into the track by using the simplified privacy quantization protection model.
4. The method of claim 3, wherein the pruning reduction process is:
step A1, training the privacy quantification protection model obtained by using the training sample, and determining the parameter lambda of the privacy quantification protection model0,λ1,λ2,…,λqStoringIn a parameter sequence lambda*And a sequence of buffer parameters lambdatIts optimization function value is expressed as SSE*(ii) a Storing M groups of training samples in variable array A*And cache variable array At；
Step A2, for parameter sequence λ*The privacy quantization protection model is formed, and each variable is calculated based on a variable array A*Correlation coefficient R with labelkAll correlation coefficients R to be obtainedkIs stored in a list R*(ii) a Wherein the correlation coefficient RkThe calculation formula of (2) is as follows:
Figure FDA0002682839900000022
in the formula, xi,kIs a variable array A*K variable value, y of the ith training sample setiFor the label value corresponding to the ith set of training samples,
Figure FDA0002682839900000023
is a variable array A*The k-th variable mean of the middle M training samples,
Figure FDA0002682839900000024
the average value of M label values corresponding to M groups of training samples is obtained;
step A3, from List R*Selecting the minimum correlation coefficient, setting the subscript as s, and combining the minimum correlation coefficient RsAnd comparing with a preset correlation coefficient threshold value:
if the minimum correlation coefficient RsIf the correlation coefficient is larger than a preset correlation coefficient threshold value, the parameter sequence lambda is determined to be*The formed privacy quantification protection model is used as a final simplified privacy quantification protection model;
if the minimum correlation coefficient RsIf the correlation coefficient is less than or equal to the preset correlation coefficient threshold value, the parameter sequence lambda is cachedtParameter lambda with middle subscript ssSet to 0, buffer variable array AtVariable value slave cache variable array with middle subscript of sAtGet out of the buffer, and then buffer the variable array AtAnd sample label is substituted into a buffer parameter sequence lambdatThe formed privacy quantitative protection model calculates the optimization function value and stores the optimization function value in the SSEtComparison of SSEtAnd SSE*：
If SSEt>SSE*Then the parameter sequence λ is maintained*Without change, will be represented by a parameter sequence λ*The formed privacy quantification protection model is used as a final simplified privacy quantification protection model;
if SSEt≤SSE*Then the parameter sequence is lambda*Updating to and caching parameter sequence lambdatSame, variable array A*Update to and cache variable array AtSame, SSE*Is replaced with SSEtAnd returning to the step A2.
5. The method according to claim 1, wherein the specific method for statistically calculating the privacy protection degree of each group of track data sets according to the prediction result is as follows:
let i group trajectory data set
Figure FDA0002682839900000031
Middle LDL corresponding to the strip trackDThe prediction accuracy of the G partitions distributed in the track is P from large to smalli,1,Pi,2,…,Pi,GTogether forming an ith set of trajectory data sets
Figure FDA0002682839900000032
To a sequence of accuracies
Figure FDA0002682839900000033
Then according to the accuracy sequence
Figure FDA0002682839900000034
Calculating the privacy protection degree WAE of each group of track data setsi：
Figure FDA0002682839900000035
Wherein, P'gFor historical track data set DtrainPrediction accuracy at the g-th partition.
6. The method of claim 1, wherein the predetermined function used to generate the derived value according to the degree of privacy protection comprises any one or more of: y is x2，y＝x3，y＝x-1，y＝x-2，
Figure FDA0002682839900000036
y is log x, y is ln x; wherein x is the privacy protection degree and y is the derived value.
7. The method of claim 1, wherein the predetermined noise floor radius r isbase＝500m。
8. An apparatus comprising a processor and a memory; wherein: the memory is to store computer instructions; the processor is configured to execute the computer instructions stored by the memory, in particular to perform the method according to any one of claims 1 to 7.
9. A computer storage medium storing a program which, when executed, performs the method of any one of claims 1 to 7.
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