CN112118531A - Privacy protection method of crowd sensing application based on position - Google Patents
Privacy protection method of crowd sensing application based on position Download PDFInfo
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- CN112118531A CN112118531A CN202010957163.4A CN202010957163A CN112118531A CN 112118531 A CN112118531 A CN 112118531A CN 202010957163 A CN202010957163 A CN 202010957163A CN 112118531 A CN112118531 A CN 112118531A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/025—Services making use of location information using location based information parameters
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- G—PHYSICS
- 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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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
- H04W12/02—Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
Abstract
The invention belongs to the technical field of privacy protection, and discloses a privacy protection method of crowd sensing application based on position, which comprises the following steps of firstly, aggregating the geographical positions of all users contained in a sensed area by taking an interested place as a unit; step two, the users covered by each interested place disturb the geographic position and the perception data value of the users locally on the intelligent equipment of each user, and provide the disturbed geographic position and perception data value to the central platform; thirdly, the central platform carries out re-aggregation on all received sensing data values according to the aggregation method in the first step, and then carries out true value discovery operation on each interested place according to the sensing data values obtained by re-aggregation.
Description
Technical Field
The invention relates to the technical field of privacy protection, in particular to a privacy protection method of crowd sensing application based on positions.
Background
With the rapid development of the internet of things and embedded sensors, mobile smart devices are equipped with various sensors (such as microphones, accelerometers, compasses), which can be used to collect numerous and complicated fine-grained sensing data in a city range, and thus, the concept of mobile crowd sensing is brought forward. A typical crowd sensing system generally comprises a central platform located at a cloud and a plurality of mobile smart device users distributed widely, as shown in fig. 1, the smart device users collect and upload sensing data to the cloud according to sensing tasks issued by the platform, and the platform collects a large amount of sensing data and excavates valuable information. The crowd sensing system has a plurality of application scenes in practice, wherein the operation of monitoring different places and performing truth value discovery based on sensing data is one typical application, the values of sensed objects (such as environmental noise and air quality) in different places are different in a given sensing area range, an intelligent device user can sense the sensed objects of the places to which the intelligent device user belongs in the moving process and report the sensing data to a platform, then the platform aggregates the sensing data values of the intelligent device according to the positions reported by the user, and the platform discovers real values of the sensed objects by utilizing truth values by considering that the user sensing data quality is different.
One major problem with collecting data using crowd sensing is that the privacy of the user of the smart device may be compromised during the process of collecting the sensed data, all data submitted by the user (e.g., location, sensed data value) may reveal their sensitive information (e.g., preferences, behavior habits), and lawbreakers may use the sensitive information to make a debt, and even threaten the security of the user's lives and properties. Therefore, privacy protection mechanisms must be provided for the user's location and perceived data values, but the problem with this is that the usefulness of the data collected by the platform is often compromised by the addition of privacy protection mechanisms. Therefore, how to extract useful information from disturbed or processed data while providing privacy protection for users is an urgent problem to be solved.
Disclosure of Invention
The invention provides a privacy protection method of crowd sensing application based on position, which solves the problems of how to extract useful information from processed data while performing privacy protection.
The invention can be realized by the following technical scheme:
a privacy protection method of location-based crowd sensing application comprises the following steps:
step one, aggregating the geographical positions of all users contained in the perceived area by taking an interested place as a unit;
step two, the users covered by each interested place disturb the geographic position and the perception data value of the users locally on the intelligent equipment of each user, and provide the disturbed geographic position and perception data value to the central platform;
thirdly, the central platform carries out re-aggregation on all received sensing data values according to the aggregation method in the first step, and then carries out true value discovery operation on each interested place according to the sensing data values obtained by re-aggregation.
Further, m interest places are arranged in the perceived area, and the geographic position of the jth interest place is recorded as LjAnd its set is denoted as L ═ L1,L2,...,LmM, where j is 1.. m, at the point of interest LjIs denoted as UjThen the user subset UjAll users u iniGeographic location liAll with the geographic position L of the jth interest sitejInstead, where i ═ 1.., n.
Further, disturbing the original geographic position of the user to any other interested place in the current sensing area by using the specified position disturbing probability to finish the disturbance of the geographic position; the disturbance of the perception data value is completed by adding Gaussian noise to the perception data value collected by the user.
Further, using the following formula, for user uiGeographic location liAnd a perceptual data value viScrambling was performed and the location after scrambling was recorded asThe perception data value is recorded as
Wherein p represents the position disturbance probability set by the crowd sensing system,representing scrambling as dividing user uiOriginal geographic location liAny other interested place L of the current arearThe probability of (d);
wherein the content of the first and second substances,representing Gaussian noise, obeying a mean of 0 and a variance ofIs the user uiPrivate distribution, variance ofBy each user uiFrom a predetermined parameter provided by the crowd sensing systemIs sampled in an exponential distribution of lambda.
Further, the central platform uses the location of interest LjFor all received geographical positionsThe place of interest L is aggregated againjCovered subset of usersCompleting the truth finding operation in the third step for all the interested places according to the following steps,
step I, initializing user subset when executing truth value discovery operationWeight of each user in (1)Are all 1;
step II, using the following equation, for the user subsetThe sensing data value submitted by each user in the system is subjected to true value estimation operation to obtain an estimation value
Step III, utilizing the following equation, obtaining the estimated value according to the step II for the weight of each userThe updating is carried out, and the updating is carried out,
and IV, repeatedly executing the steps II to III until the iteration times t reach a set value, and finishing the truth value finding operation.
Further, the set value is set to 8 or more.
The beneficial technical effects of the invention are as follows:
(1) according to the method and the device, the risk that privacy is leaked in the position and perception data values submitted by the intelligent equipment user in the position-based crowd sensing application is considered, so that privacy protection mechanisms meeting differential privacy are provided for the two types of data respectively to protect the personal privacy of the user.
(2) The privacy protection mechanism provided by the invention is respectively specific to the position and the continuous sensing data value, can be applied to the true value discovery of the crowd sensing system based on the position, and can also be applied to the crowd sensing system monitored in real time, as long as participating users execute the same operation in each time slice, thus, other privacy (such as track privacy) of the users can be effectively protected.
(3) The derivation proves that the position privacy protection mechanism and the perception data value privacy protection mechanism respectively meet the local differential privacy. Besides, experiments prove that the privacy protection method is feasible.
Drawings
FIG. 1 is a schematic diagram of a prior art location-based crowd sensing application;
fig. 2 is a flow chart of a privacy protecting method of the present invention.
Detailed Description
The following detailed description of the preferred embodiments will be made with reference to the accompanying drawings.
The invention provides a method for solving the problem of user privacy disclosure in the location-based crowd sensing application, so as to achieve the true value of a region needing to be sensed, which can be accurately estimated by using a method of finding out the true value on the premise of protecting user sensitive information. According to the method, the situation that the sensitive information of the user can be leaked by the position and the perception data value submitted by the user is considered, so that the invention provides a privacy protection mechanism based on local differential privacy for the geographical position and the perception data value which need to be submitted by the user respectively, as shown in fig. 1 and 2, the method specifically comprises the following steps:
step one, aggregating the geographical positions of all users contained in the perceived area by taking an interested place as a unit;
in order to represent the geographic position of a smart device user and the position of a perception object, m interest positions are virtually combined into a perceived area, and the geographic position of the jth interest position is recorded as LjThe set of all interest points is L ═ L1,L2,...,LmLet us consider a crowd-sourcing system with a subset of n registered users, U ═ U1,U2,...,UnMeans, note that it is at the point of interest LjIs denoted as UjIf user ui∈UjThen its geographical location liUse the location of interest LjTo approximate substitution, i.e. li=Lj. In addition to this, user uiThe value of the collected perception data is denoted as viThe perception data value is related to the place of interest of the user. Each participating user needs to submit their personal identification, location stamp, and sensory data values to the central platform.
Step two, the users covered by each interested place disturb the geographic position and the perception data value of the users locally on the intelligent equipment of each user, and provide the disturbed geographic position and perception data value to the central platform;
considering that an untrusted central platform may sell user information for business profit making and a database of a crowd sensing system may be attacked, there is a risk of privacy disclosure when a user submits a sensing data value and a location, and therefore, in order to protect the personal privacy of the user, a privacy protection mechanism is provided for the location and the sensing data value which the user of an intelligent device needs to submit, respectively. First, it is necessary to scramble the user's location dataIn disorder, the crowd-sourcing system publishes a probability p of location perturbation, user uiMaking its original position l according to the position disturbance probability p provided by the crowd sensing systemiDisorder isWherein the original position l is maintained with a probability of 1-piAnd disturbing the interest location L of any current area except the original position of the user by the probability of p/(k-1)r∈L\{Lj}. The geographical location disturbance probability distribution of the user is as follows:
the location privacy protection mechanism satisfies1-local differential privacy.1The smaller the privacy protection degree, the higher the location privacy protection mechanism1Ln ((1-p) (m-1)/p). The greater the probability p of disruption for the geographic location or the greater the number m of places of interest, the lower the degree of privacy protection. When the geographical location privacy protection mechanism is operated, a reasonable privacy protection degree needs to be set according to a specific application scene, so that the location disturbance probability p is set.
Secondly, we also need privacy protection on the perception data value, user uiUsing a value sampled in an exponential distribution (lambda) provided by a crowd-sourcing sensing systemAs user uiThe variance of the private Gaussian distribution, and unifying the mean value of the Gaussian distribution of all users as 0, and then sampling the private distribution by the users to obtain a value tauiAs noise added to the raw perceptual data value viTo obtain a noisy sensed data valueThe above process can be expressed as follows:
the perceptual data value privacy protection mechanism satisfies (a)2-local differential privacy, wherein,2the smaller, the higher the degree of privacy protection. In the mechanism of privacy protection of perceptual data values For a user at a place of interest LjSensitivity in time, i.e. the difference between the largest arbitrary two data values for this location of interest. Variance of user samplesThe larger the size of the tube is,2the smaller the degree of privacy protection of the user. Besides, when the parameter λ of the exponential distribution is smaller, the lower bound is meant to be larger, and the degree of privacy protection is lower. When the perception data value privacy protection mechanism is operated, a reasonable privacy protection degree needs to be set according to a specific application scene, so that a specific parameter lambda of exponential distribution is given.
Thirdly, the central platform carries out re-aggregation on all received sensing data values according to the aggregation method in the first step, and then carries out true value discovery operation on each interested place according to the sensing data values obtained by re-aggregation.
After each participating user submits the data after the respective processing, the central platform firstly aggregates all the perception data values according to the geographical position data, namely, the perception data values of an interested place are aggregated together, and then the operation of finding the true value of each interested place is carried out according to the perception data values obtained by aggregation. Truth discovery is a method proposed to take into account the quality of data submitted by a plurality of users, and includes two iterative steps of estimating user weights and updating user weights for truth values of perceptsAnd repeating iteration until reaching the convergence standard, wherein the convergence standard of the invention is based on the iteration times t, and when the iteration times t is more than or equal to the set value, ending the iteration. Specifically, the central platform uses a location of interest LjFor all received geographical positionsTo be aggregated again, i.e. corresponding sensed data valuesThe aggregation is carried out, then the location of interest LjCovered subset of usersCompleting the truth finding operation in the third step for all the interested places according to the following steps,
step I, initializing user subset when executing truth value discovery operationWeight of each user in (1)Are all 1;
step II, using the following equation, for the user subsetThe sensing data value submitted by each user in the system is subjected to true value estimation operation to obtain an estimation value
Step III, utilizing the following equation, obtaining the estimated value according to the step II for the weight of each userThe updating is carried out, and the updating is carried out,
and IV, repeatedly executing the steps II to III until the iteration time t reaches a set value which can be set to be more than or equal to 8, ending the iteration process and finishing the truth value discovery operation.
And finally, obtaining the estimated value of all the perceived objects in the whole perceived area to provide the estimated value for the data demand side.
The privacy protection method based on the local differential privacy completes the process of discovering the true value of the perceived object, and comprises two mechanisms, namely a position privacy protection mechanism and a perceived data value privacy protection mechanism. The position privacy protection mechanism mainly scrambles the original geographic position of a user to any interested place of other current sensing areas with a certain disturbance probability, the sensing data value privacy protection mechanism achieves the privacy protection effect by adding Gaussian noise to the sensing data value collected by the user, namely, a central platform needs to give a preset parameter, a position disturbance probability p and an exponential distribution parameter lambda, wherein the position privacy protection mechanism and the sensing data value privacy protection mechanism respectively meet the condition that the position belongs to the element of E1-local differential privacy sum e2, -local differential privacy, probability of location perturbation p and exponential distribution parameter λ need to be respectively according to ∈1And e2And setting. Then, the central platform aggregates the noisy perception data values of the user according to the disturbed geographical position submitted by the user, processes the data values according to a method found by a truth value, and finally obtains a true value of the whole perception area.
Although specific embodiments of the present invention have been described above, it will be appreciated by those skilled in the art that these are merely examples and that many variations or modifications may be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is therefore defined by the appended claims.
Claims (6)
1. A privacy protection method of a location-based crowd sensing application is characterized by comprising the following steps:
step one, aggregating the geographical positions of all users contained in the perceived area by taking an interested place as a unit;
step two, the users covered by each interested place disturb the geographic position and the perception data value of the users locally on the intelligent equipment of each user, and provide the disturbed geographic position and perception data value to the central platform;
thirdly, the central platform carries out re-aggregation on all received sensing data values according to the aggregation method in the first step, and then carries out true value discovery operation on each interested place according to the sensing data values obtained by re-aggregation.
2. The privacy protection method for location-based crowd sensing applications as claimed in claim 1, wherein: setting m interest places in the perceived area, and recording the geographic position of the jth interest place as LjAnd its set is denoted as L ═ L1,L2,...,LmM, where j is 1.. m, at the point of interest LjIs denoted as UjThen the user subset UjAll users u iniGeographic location liAll with the geographic position L of the jth interest sitejInstead, where i ═ 1.., n.
3. The privacy protection method for location-based crowd sensing applications as claimed in claim 2, wherein: disturbing the original geographic position of the user to any other interested place of the current sensing area by using the specified position disturbing probability to finish the disturbance of the geographic position; the disturbance of the perception data value is completed by adding Gaussian noise to the perception data value collected by the user.
4. The privacy protection method for location-based crowd sensing applications as claimed in claim 3, wherein: using the following formula for user uiGeographic location liAnd a perceptual data value viScrambling was performed and the location after scrambling was recorded asThe perception data value is recorded as
Wherein p represents the position disturbance probability set by the crowd sensing system,representing scrambling as dividing user uiOriginal geographic location liAny other interested place L of the current arearThe probability of (d);
5. The privacy protection method for location-based crowd sensing applications as claimed in claim 4, wherein the central platform uses a location of interest LjFor all received geographical positionsThe place of interest L is aggregated againjCovered subset of usersThe truth finding operation in the third step comprises the following steps:
step I, initializing user subset when executing truth value discovery operationWeight of each user in (1)Are all 1;
step II, using the following equation, for the user subsetThe sensing data value submitted by each user in the system is subjected to true value estimation operation to obtain an estimation value
Step III, using the following equation, the weight basis for each userEstimated value obtained in step IIThe updating is carried out, and the updating is carried out,
and IV, repeatedly executing the steps II to III until the iteration times t reach a set value, and finishing the truth value finding operation.
6. The privacy protection method for location-based crowd sensing applications as claimed in claim 5, wherein: the set value is set to 8 or more.
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