CN109769212B - Track privacy protection method based on slice in crowd-sourcing perception - Google Patents
Track privacy protection method based on slice in crowd-sourcing perception Download PDFInfo
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Abstract
The invention is suitable for the technical field of privacy protection, and provides a track privacy protection method based on slices in crowd-sourcing perception, which comprises the following steps: s1, slicing the track data by taking the position as a minimum unit to obtain track tuples and generate identifiers of the track tuples; s2, forming query logic by mapping the track tuple index and the operation label, and storing the query logic in the client; s3, uploading the track tuples and the corresponding indexes to a server through a random threshold number of clients; s4, acquiring an index sequence of the track tuple based on the query logic, and sending the index sequence to the server; and S5, receiving a track tuple returned by the server, wherein the track tuple is a track tuple corresponding to the index sequence. The track tuples iterate among the participants, the personal position privacy and the track privacy of the perception users can be effectively protected, the problem of track privacy disclosure caused by collusion of malicious users and a server is solved, tracks can be rebuilt at clients, and the practicability of client data is guaranteed.
Description
Technical Field
The invention belongs to the technical field of privacy protection, and provides a track privacy protection method based on slices in crowd-sourcing perception.
Background
The concept of Crowd Sensing (CS) is proposed based on the subjective initiative of a person in an activity. Specifically, they propose that a mobile intelligent terminal held by an individual can be used as a mobile sensing node for completing collection, analysis and sharing of various local data (such as voice, video, image and the like). The current use of health-engaged sensing as a means of data collection is rapidly becoming a reality, which will drastically change the size and type of data that can be aggregated for several demographic health, epidemiological, statistical, and data analysis purposes. Crowd sensing has a wide range of possibilities, but previous work on crowd sensing has not considered in detail applications that may arise in a healthy setting. Wearable technology is one of the biggest applications of thing networking, and the popularization degree of wearable equipment is explosion-type growth at present, can use more sensors to record each aspect of our daily life, and our life has been influenced in an unconscious mode. However, with the widespread deployment of wearable devices, security problems arise, and the most serious threat is privacy disclosure of wearable device data information and trajectory information, because the information contains abundant private information of individuals.
Researchers at home and abroad also provide a plurality of solutions for protecting the track privacy in crowd sensing. The SLICER method proposed by fqiu and the like is the first k anonymous privacy protection scheme for multimedia data crowd-sourcing perception, integrates a data coding technology and a message exchange strategy, can effectively protect the privacy of participants, and simultaneously keeps high data accuracy. But this approach makes the data non-reproducible at the client. As a consensus, one of the key challenges of privacy protection is how to achieve effective privacy protection while maintaining data utility. The benefits of the crowd sensing application system are directly derived from the value of the crowd sensing data set, and meaningful findings are obtained. For example, the personal movement track is rendered on a map and can be used for making a decision for selecting a public fitness facility construction area, and a plurality of existing track privacy protection methods are available, so that the purpose of track privacy protection is achieved, but the practicability of track data at a client cannot be guaranteed.
Disclosure of Invention
The embodiment of the invention provides a track privacy protection method based on slicing in crowd-sourcing perception, which realizes the reappearance of track data at a client and ensures the practicability of the track data at the client.
In order to achieve the above object, the present invention provides a method for protecting track privacy based on slice in crowd-sourcing perception, the method comprising the following steps:
s1, slicing the track data by taking the position as a minimum unit, acquiring track tuples, and generating identifiers of the track tuples;
s2, mapping the track tuple index and the operation label to form a query logic, and storing the query logic in the client, wherein the index is the track tuple identifier;
s3, uploading the track tuples and the corresponding indexes to a server through a random threshold number of clients;
s4, acquiring an index sequence of the track tuple based on the query logic, and sending the index sequence to the server;
and S5, receiving a track tuple returned by the server, wherein the track tuple is a track tuple corresponding to the index sequence.
Further, to prevent the server from launching an attack based on time processing, if the operation tag in step S2 is a timestamp, the method further includes, before step S3: disturbing the timestamp of the track tuple, and simultaneously keeping the mapping of the original time and the disturbed time by the client; further included after step S5 is: and restoring the timestamp of the track tuple to reconstruct the track data.
Further, when uploading the track tuple, the client randomly selects or generates three parameters α, β, λ, where α is an initial value, λ is a step length, β is a threshold, the value of α decreases by λ every time the track tuple passes through one participating client, and when α is smaller than the value of β, the participating client uploads the track tuple to the server.
Further, the server detects whether the indexes in the index sequence conform to a set standard format, if not, the server disconnects the corresponding client, if so, the server detects whether the UUID corresponding to the index sequence exists in the server, if not, the server disconnects the corresponding client, and if so, the track tuple corresponding to the index sequence is returned to the client.
The motion track privacy protection method based on the slice in the crowd sensing has the following beneficial effects:
1. the track tuples are iterated in the participants, so that the personal position privacy and the track privacy of the user can be effectively protected and perceived, and the problem of track privacy disclosure caused by collusion of a malicious user and a server is solved.
2. The track can be reconstructed at the client, so that the practicability of the client data is ensured;
3. the encryption and decryption processes are not needed, the calculation cost is low, and the method is safe and efficient.
Drawings
Fig. 1 is a flowchart of a track privacy protection method based on slices in crowd sensing according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Crowd's wisdom perception system: a large-scale data acquisition system using a large amount of mobile terminal sensing data is provided, wherein a crowd sensing system is composed of a crowd sensing application server at the cloud end and a batch of mobile terminal users.
A server: a server that obtains sensory data of participants without exposing participant location and trajectory privacy.
The method and the system make full use of the participants to carry out the iteration of the track tuples, ensure the position privacy security of the user and solve the threat of privacy disclosure caused by collusion attack between malicious participants and the server. In addition, the user can realize track reconstruction at the client, and the practicability of the client data is guaranteed.
Fig. 1 is a flowchart of a track privacy protection method based on slices in crowd sensing according to an embodiment of the present invention, where the method includes the following steps:
s1, slicing the track data by taking the position as a minimum unit, acquiring track tuples, and generating identifiers of the track tuples;
the client slices the acquired track data by taking a position (GPS) as a minimum unit, and divides the track data into track tuples represented as TTi＝(xi，yi，ti，si，hi) I is 1,2,3 … n, wherein (x)i，yi) Represents tiPosition coordinates of time of day, si，hiRespectively represent tiThe motion speed and the heart rate at the moment, and the generated Universal Unique Identifiers (UUIDs) are allocated to each track tuple, wherein the TTI is the index of the track tuple, namely the identifier UUID of the track tuple;
s2, forming query logic by mapping the indexes of the track tuples and the operation labels, and storing the query logic in the client;
in the implementation of the present invention, each trace tuple is mapped with at least one operation tag, for example, a location-based operation tag and a timestamp-based operation tag, an identifier UUID of the trace tuple and the mapping of the UUID and the operation tag form a query logic, and the query logic is stored in a client.
S3, uploading the track tuples and the indexes thereof to a server through a random threshold number of clients;
when a user uploads a track tuple and an index thereof to a server, three random numbers are randomly generated at the moment, wherein the three random numbers are respectively alpha, beta and lambda, alpha is an initial value, lambda is a reduced value, and beta is a threshold value, the value of alpha is reduced by lambda when the track tuple and the index thereof pass through one participant, and when the value of alpha is reduced to be smaller than the value of beta, the participant at the moment can directly upload the track tuple and the index thereof to the server. Different users may select different values of α, β, λ.
S4, acquiring an index sequence of the track tuple based on the query logic, sending the index sequence to a server, and searching a corresponding track tuple sequence based on the index by the server;
in the embodiment of the invention, based on the security of data, the server needs to perform format detection on the index sequence uploaded by the user, namely, the access of illegal users is avoided. The method comprises the steps that the format of the UUID is checked, if the format of the UUID does not accord with the set standard format, the server can be disconnected immediately, if the format of the UUID meets the set standard format, whether the UUID corresponding to an index sequence exists in the server is detected, if the UUID does not exist, the connection between the server and a corresponding client side is disconnected immediately, and otherwise, a track tuple corresponding to a retrieval sequence is returned to a user.
And S5, receiving the track tuple returned by the server.
In the embodiment of the present invention, since the timestamp provides key information to an adversary in an attack process, if the operation tag of the trace tuple is the timestamp, the method further includes, before step S3:
disturbing the timestamp of the track tuple, storing the mapping of the original time and the disturbed time in the client, wherein the track tuple is marked as TTi＝(xi，yi，ti’，si，hi) I-1, 2,3 …. n, wherein ti' is a perturbed timestamp that would prevent an attacker or server from launching a timestamp-based attack; correspondingly, the method also comprises the following steps after the step S5:
and restoring the timestamp of the track tuple to reconstruct the track data.
The motion track privacy protection method based on the slice in the crowd sensing has the following beneficial effects:
1. the track tuples are iterated in the participants, so that the personal position privacy and the track privacy of the user can be effectively protected and perceived, and the problem of track privacy disclosure caused by collusion of a malicious user and a server is solved.
2. The track can be reconstructed at the client, so that the practicability of the client data is ensured;
3. the encryption and decryption processes are not needed, the calculation cost is low, and the method is safe and efficient.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (3)
1. A track privacy protection method based on slices in crowd sensing is characterized by comprising the following steps:
s1, slicing the track data by taking the position as a minimum unit, acquiring track tuples, and generating identifiers of the track tuples;
s2, forming query logic by the indexes of the track tuples and the mapping of the track tuples and the operation labels, and storing the query logic in a client, wherein the indexes are identifier rows of the track tuples;
s3, uploading the track tuples and the corresponding indexes to a server through a random threshold number of clients;
s4, acquiring an index sequence of the track tuple based on the query logic, and sending the index sequence to the server;
s5, receiving a track tuple returned by the server, wherein the track tuple is a track tuple corresponding to the index sequence;
when the track tuple is uploaded, the client randomly selects or generates three parameters alpha, beta and lambda, wherein alpha is an initial value, lambda is a step length, beta is a threshold value, the value of alpha is reduced by lambda every time the track tuple passes through one participating client, and when alpha is smaller than the value of beta, the participating client uploads the track tuple to the server.
2. The method for slice-based trajectory privacy protection in crowd sensing as claimed in claim 1, wherein if the operation tag in step S2 is a timestamp, before step S3, the method further comprises: disturbing the timestamp of the track tuple, and simultaneously reserving mapping of original time and disturbed time at the client; further included after step S5 is: and restoring the timestamp of the track tuple to reconstruct the track data.
3. The method as claimed in claim 1 or 2, wherein the server detects whether the indexes in the index sequence conform to a set standard format, if not, the server disconnects from the corresponding client, if so, the server detects whether the UUID corresponding to the index sequence exists in the server, if not, the server disconnects from the corresponding client, and if so, the trace tuple corresponding to the index sequence is returned to the client.
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| CN107229872A (en) * | 2016-03-26 | 2017-10-03 | 肖哲 | It is a kind of to separate storage query logic and the private data guard method of segment data |
| CN109165527A (en) * | 2018-08-28 | 2019-01-08 | 东北大学 | Support the track protecting sensitive data method of personalized privacy |
| CN109214205A (en) * | 2018-08-01 | 2019-01-15 | 安徽师范大学 | Position and data-privacy guard method in a kind of intelligent perception based on k- anonymity |
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| JP2010205059A (en) * | 2009-03-04 | 2010-09-16 | Oki Electric Ind Co Ltd | Information providing server, information providing server system, information providing method and program |
| KR101847370B1 (en) * | 2012-06-15 | 2018-05-24 | 알까뗄 루슨트 | Architecture of privacy protection system for recommendation services |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101873317A (en) * | 2010-06-07 | 2010-10-27 | 孟小峰 | Position privacy protection method for perceiving service quality |
| CN104219661A (en) * | 2014-09-01 | 2014-12-17 | 北京邮电大学 | TDOA (time difference of arrival) location tracking resistant source location privacy protection routing method |
| CN107229872A (en) * | 2016-03-26 | 2017-10-03 | 肖哲 | It is a kind of to separate storage query logic and the private data guard method of segment data |
| CN109214205A (en) * | 2018-08-01 | 2019-01-15 | 安徽师范大学 | Position and data-privacy guard method in a kind of intelligent perception based on k- anonymity |
| CN109165527A (en) * | 2018-08-28 | 2019-01-08 | 东北大学 | Support the track protecting sensitive data method of personalized privacy |
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