A method and system for protecting personal privacy data security of intelligent connected vehicles

Abstract

The present invention relates to the field of data transmission technology, and specifically to a method and system for protecting personal privacy data security of smart connected vehicles. The method includes: obtaining the traffic information of cars in each sub-area of ​​the target area; determining the query sensitivity according to the traffic information of cars in each sub-area; combining the changes in the VIN information of cars in the neighborhood of each sub-area in adjacent time periods, determining the query sensitive area corresponding to each sub-area in each time period; determining the number of false location information to be generated in the internal area of ​​the query sensitive area and the number of false location information to be generated in the external area of ​​each sub-area in each time period according to the query sensitivity, and then generating a corresponding false location set. The present invention reduces the risk of vehicle location information being leaked and improves the security of the car location privacy information of smart connected users.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and in particular to a method and system for protecting personal privacy data security of an intelligent network-connected vehicle. Background Art

[0002] Intelligent connected vehicles are a new type of transportation that integrates multiple advanced technologies such as communication technology and electronic technology. Therefore, solving the location privacy problem of users in the Internet of Vehicles has important theoretical and practical significance for the development of the Internet of Vehicles and the improvement of the network system.

[0003] Although the existing fake location selection algorithm for the Internet of Vehicles involves the protection of the location privacy of the Internet of Vehicles by historical query probability and location semantics, it does not take into account the changes in query probability in different time periods. It also ignores the problem that the daily user access trends of the same location unit in the continuous time background are quite different. Attackers will distinguish the generated fake locations according to the spatial sensitivity of different time periods in a day, which makes the car's personal privacy data at risk of leakage. Summary of the invention

[0004] In order to solve the problem that the existing Internet of Vehicles fake location selection algorithm still has the risk of leakage when protecting automobile privacy data, the purpose of the present invention is to provide a method and system for protecting personal privacy data of intelligent networked vehicles. The technical solution adopted is as follows:

[0005] In a first aspect, the present invention provides a method for protecting personal privacy data of an intelligent connected vehicle, the method comprising the following steps:

[0006] Obtain the traffic information of cars in each sub-area in the target area;

[0007] Determine the query sensitivity of each sub-area in each time period based on the traffic information of the cars in each sub-area; determine the query sensitive path corresponding to each sub-area in each time period based on the change of the car VIN information in the neighborhood of each sub-area in adjacent time periods; determine the query sensitive area based on the query sensitive path;

[0008] According to the query sensitivity of each sub-region in each time period, the number of false location information to be generated for each sub-region in each time period is obtained; based on the area ratio of the query sensitive area corresponding to each sub-region in each time period and the number of false location information to be generated, the number of false location information to be generated in the inner area and the number of false location information to be generated in the outer area of ​​the query sensitive area of ​​each sub-region in each time period is determined;

[0009] A corresponding set of false positions is generated based on the amount of false position information to be generated in the inner area and the amount of false position information to be generated in the outer area.

[0010] Preferably, determining the query sensitivity of each sub-area in each time period according to the traffic information of the cars in each sub-area includes:

[0011] The ratio between the number of times that the positions of all vehicles sent to the terminal via the vehicle GPS in the candidate time period are the sub-area to be analyzed and the total number of times that the positions of all vehicles sent to the terminal via the vehicle GPS in the preset time interval are the sub-area to be analyzed is used as the query sensitivity of the sub-area to be analyzed in the candidate time period;

[0012] The sub-region to be analyzed is any sub-region in the target region, and the candidate time period is any time period in a preset time interval.

[0013] Preferably, the step of determining the query sensitive path corresponding to each sub-area in each time period by combining the changes in the automobile VIN information in the neighborhood of each sub-area in adjacent time periods includes:

[0014] According to the relationship between the number of VIN information contained in each time period of the sub-region to be analyzed and the number of VIN information contained in the next time period corresponding to each sub-region in the neighborhood of the sub-region to be analyzed, the possibility value of each sub-region in the neighborhood of the sub-region to be analyzed in each time period being a trend block is obtained;

[0015] The possibility value is used to screen the query sensitive paths corresponding to the sub-area to be analyzed in the candidate time period.

[0016] Preferably, obtaining the possibility value of each sub-region in the neighborhood of the sub-region to be analyzed in each time period being a trend block according to the relationship between the amount of VIN information contained in each time period of the sub-region to be analyzed and the amount of VIN information contained in the next time period corresponding to each sub-region in the neighborhood of the sub-region to be analyzed, includes:

[0017] The ratio between the number of types of VIN information contained in the t+1th time period of each sub-region in the neighborhood of the sub-region to be analyzed and the number of types of VIN information contained in the tth time period of the sub-region to be analyzed is taken as the possibility value that the corresponding sub-region in the neighborhood of the sub-region to be analyzed in the tth time period is a trend block.

[0018] Preferably, using the possibility value to screen the query sensitive path corresponding to the sub-area to be analyzed in each time period includes:

[0019] The sub-region with the largest possibility value in the neighborhood of the sub-region to be analyzed in the t-th time period is taken as the first trend block of the sub-region to be analyzed, the sub-region with the largest possibility value in the neighborhood of the first trend block of the sub-region to be analyzed in the t+1-th time period is taken as the second trend block of the sub-region to be analyzed, the sub-region with the largest possibility value in the neighborhood of the second trend block of the sub-region to be analyzed in the t+2-th time period is taken as the third trend block of the sub-region to be analyzed, and so on, until the possibility value is less than the preset possibility threshold;

[0020] All trend blocks of the sub-region to be analyzed constitute the query sensitive path corresponding to the sub-region to be analyzed in the tth time period.

[0021] Preferably, determining the query sensitive area based on the query sensitive path includes:

[0022] A straight line segment between a starting point and an ending point of a query sensitive path corresponding to the sub-region to be analyzed in the tth time period is obtained, and a closed area enclosed by the straight line segment and the query sensitive path is used as a query sensitive area corresponding to the sub-region to be analyzed.

[0023] Preferably, obtaining the amount of false location information to be generated for each sub-area in each time period according to the query sensitivity of each sub-area in each time period includes:

[0024] Calculate the difference between the query sensitivity of the sub-area to be analyzed in the tth time period and the minimum query sensitivity of all sub-areas in the tth time period; subtract a constant 1 from the reciprocal of the difference to obtain an adjustment coefficient;

[0025] Obtaining a difference between a preset maximum value of the number of false location information to be generated and a preset minimum value of the number of false location information to be generated; calculating a rounding-down result of the product of the difference and the adjustment coefficient, and calculating a sum of a preset minimum value of the number of false location information to be generated and the rounding-down result;

[0026] The minimum value between the sum and the maximum value of the preset number of false location information to be generated is used as the number of false location information to be generated in the sub-area to be analyzed in the tth time period.

[0027] Preferably, the method of determining the amount of false location information to be generated in the inner area and the outer area of ​​the query sensitive area of ​​each sub-area in each time period in combination with the area ratio of the query sensitive area corresponding to each sub-area in each time period and the amount of false location information to be generated includes:

[0028] The product of the area ratio of the query sensitive area corresponding to the sub-area to be analyzed in the tth time period and the amount of false location information to be generated in the sub-area to be analyzed in the tth time period is rounded down to determine the amount of false location information to be generated in the inner area of ​​the query sensitive area of ​​the sub-area to be analyzed in the tth time period;

[0029] The difference between the amount of false location information to be generated in the sub-region to be analyzed in the t time period and the amount of false location information to be generated in the inner area of ​​the sensitive area queried by the sub-region to be analyzed in the t time period is used as the amount of false location information to be generated in the outer area of ​​the sensitive area queried by the sub-region to be analyzed in the t time period;

[0030] The area ratio of the query sensitive area corresponding to the sub-area to be analyzed in the t time period is the ratio between the area of ​​the query sensitive area corresponding to the sub-area to be analyzed in the t time period and the total area of ​​the target area.

[0031] Preferably, the generating a corresponding false location set based on the amount of false location information to be generated in the internal area and the amount of false location information to be generated in the external area includes:

[0032] For each sub-region, the inner region and the outer region of the sensitive region are queried in each time period, and the corresponding false location information is generated using the k-anonymity algorithm, wherein the corresponding k value when each sub-region uses the k-anonymity algorithm to select the false location is the corresponding number of false location information to be generated;

[0033] Each sub-region queries the pseudo location information corresponding to the inner region and the pseudo location information corresponding to the outer region of the sensitive region in each time period, which together constitute a pseudo location set.

[0034] In a second aspect, the present invention provides a personal privacy data security protection system for an intelligent connected vehicle, the system comprising:

[0035] A data acquisition module is used to obtain the traffic information of cars in each sub-area in the target area;

[0036] The query sensitive area determination module is used to determine the query sensitivity of each sub-area in each time period according to the traffic information of the cars in each sub-area; determine the query sensitive path corresponding to each sub-area in each time period in combination with the change of the car VIN information in the neighborhood of each sub-area in adjacent time periods; determine the query sensitive area based on the query sensitive path;

[0037] A module for determining the number of false location information is used to obtain the number of false location information to be generated for each sub-region in each time period according to the query sensitivity of each sub-region in each time period; combining the area ratio of the query sensitive region corresponding to each sub-region in each time period and the number of false location information to be generated, to determine the number of false location information to be generated in the inner area of ​​the query sensitive region and the number of false location information to be generated in the outer area of ​​each sub-region in each time period;

[0038] The false location set generation module is used to generate a corresponding false location set based on the amount of false location information to be generated in the internal area and the amount of false location information to be generated in the external area.

[0039] The present invention has at least the following beneficial effects:

[0040] The present invention first determines the query sensitivity of each sub-area in each time period through the traffic information of each sub-area in the target area, and then considers that the position of the vehicle may change with the change of time, and there is a certain correlation between the position change of the vehicle and the time change. Therefore, the corresponding query sensitive area is determined in combination with the change of the VIN information of the car in the neighborhood of each sub-area in the adjacent time period, and the number of false location information to be generated in each sub-area in each time period is determined according to the query sensitivity of each sub-area in each time period. At the same time, the number of false location information to be generated in the inner area of ​​the query sensitive area and the number of false location information to be generated in the outer area are determined according to the area ratio of the query sensitive area, thereby generating a false location set. Compared with the existing method, the number of false location information can be adaptively determined according to the query sensitivity of each sub-area in different time periods and the query sensitive path, thereby avoiding the occurrence of too many or too small number of false location information, reducing the risk of leakage of vehicle location information, and improving the security of the vehicle location privacy information of intelligent network users. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A flowchart of a method for protecting personal privacy data of an intelligent connected vehicle provided by an embodiment of the present invention;

[0043] Figure 2 A schematic diagram of a query sensitive area provided by an embodiment of the present invention;

[0044] Figure 3 A structural block diagram of a personal privacy data security protection system for an intelligent connected vehicle provided by an embodiment of the present invention;

[0045] In the figure, a is the query sensitive path, and b is the straight line segment between the starting point and the end point of the query sensitive path. DETAILED DESCRIPTION

[0046] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, a method and system for protecting personal privacy data of an intelligent connected vehicle proposed in accordance with the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0047] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0048] The following is a detailed description of a method and system for protecting personal privacy data of an intelligent connected vehicle provided by the present invention in conjunction with the accompanying drawings.

[0049] An embodiment of a method for protecting personal privacy data security of an intelligent connected vehicle:

[0050] The specific scenario targeted by this embodiment is: when protecting the personal privacy data of smart connected vehicles, the existing fake location selection algorithm of the Internet of Vehicles does not take into account the changes in query probability in different time periods, and also ignores the problem that the daily user access trends in the same area in continuous time periods are quite different, resulting in the risk of privacy data being leaked. This embodiment calculates the query sensitivity and query sensitive path of each sub-area in each time period, and quantifies the number of fake locations that need to be generated in each sub-area in each time period, thereby avoiding the occurrence of too many or too small fake location information, thereby protecting the privacy of the location information of users of smart connected vehicles.

[0051] This embodiment proposes a method for protecting personal privacy data security of an intelligent connected vehicle. Figure 1 As shown, a method for protecting personal privacy data of an intelligent connected vehicle in this embodiment includes the following steps:

[0052] Step S1, obtaining the traffic information of the cars in each sub-area of ​​the target area.

[0053] First, GPS, Beidou and other satellite navigation receivers are installed on the intelligent networked vehicle to provide accurate location information. At the same time, the vehicle communication system is used to communicate with the terminal network, and the acquired location information is transmitted to the database of the terminal network. The location information of each car's complete driving process every day will be obtained through the vehicle communication system. The intelligent networked vehicle stores the vehicle identification number (VIN) information of the car through the internal electronic control unit (ECU), where the VIN information is the unique number of each car. When the vehicle terminal network communicates, the VIN can be exchanged as the vehicle's identity. Obtain the traffic information of all cars in the target area within the preset time interval, including the vehicle's VIN information, the vehicle's location, etc. The target area is any area on the map, and the implementer of the target area selects it according to the specific situation. For the location information of the intelligent networked vehicles in a certain target area, since the target area is often set to be large, the acquired location information data is relatively large. Therefore, in order to effectively analyze and process a large amount of data later, the target area is divided into multiple sub-areas of equal size. In specific applications, the implementer sets the size of the sub-area according to the specific situation, and the size and size of each sub-area are equal. In this embodiment, the preset time interval is a set of all historical moments whose time interval with the current moment is less than or equal to the preset duration. The preset duration is one day. In specific applications, the implementer can set the preset duration according to the specific situation.

[0054] So far, this embodiment has acquired the traffic information of the cars in each sub-area of ​​the target area within the preset time interval.

[0055] Step S2, determining the query sensitivity of each sub-area in each time period based on the traffic information of the cars in each sub-area; determining the query sensitive path corresponding to each sub-area in each time period in combination with the change of the car VIN information in the neighborhood of each sub-area in adjacent time periods; determining the query sensitive area based on the query sensitive path.

[0056] In actual applications, due to the different traffic volumes in different geographical areas, the amount of location information provided by smart connected vehicles in different areas is also different. If a certain area provides more location information, it means that the traffic volume in the area is higher. Then, when the subsequent illegal personnel conduct intrusion queries, the probability of the area being intruded is higher, and the query sensitivity of the corresponding area is higher. In addition, due to different time periods, the traffic volume of cars in the same geographical area in different time periods is also different.

[0057] First, the preset time interval is divided into multiple sub-time periods of the same duration. In this embodiment, the duration of each sub-time period is 1 hour. In specific applications, the implementer can set it according to the specific situation. Then, a sub-area in the target area and a time period in the preset time interval are analyzed as an example. Specifically, any sub-area in the target area is recorded as a sub-area to be analyzed, and any time period in the preset time interval is recorded as a candidate time period; the number of times that the position of all vehicles in the preset time interval sent to the terminal through the vehicle-mounted GPS is the sub-area to be analyzed is counted; the number of times that the position of all vehicles in the candidate time period sent to the terminal through the vehicle-mounted GPS is the sub-area to be analyzed and the total number of times that the position of all vehicles in the preset time interval sent to the terminal through the vehicle-mounted GPS is the sub-area to be analyzed is the ratio between the number of times that the position of all vehicles in the candidate time period sent to the terminal through the vehicle-mounted GPS is the sub-area to be analyzed and the total number of times that the position of all vehicles in the preset time interval sent to the terminal through the vehicle-mounted GPS is the sub-area to be analyzed is used as the query sensitivity of the sub-area to be analyzed in the candidate time period.

[0058] The greater the query sensitivity, the more false location information needs to be generated in the sub-area to be analyzed within the candidate time period, so as to avoid the real location information of the user's car from being found to a greater extent. Therefore, according to the relationship between the number of VIN information contained in each time period of the sub-area to be analyzed and the number of VIN information contained in the next time period corresponding to each sub-area in the neighborhood of the sub-area to be analyzed, the possibility value of each sub-area in the neighborhood of the sub-area to be analyzed in each time period being a trend block is obtained. Specifically, the VIN information of the vehicle corresponding to all the location information in the sub-area to be analyzed is obtained through the terminal network, and the number of types of VIN information contained in each time period of each sub-area in the neighborhood of the sub-area to be analyzed is counted. If there is the same VIN information, only one VIN information is recorded. The ratio between the number of types of VIN information contained in the t+1th time period of each sub-area in the neighborhood of the sub-area to be analyzed and the number of types of VIN information contained in the tth time period of the sub-area to be analyzed is respectively used as the possibility value of the corresponding sub-area in the neighborhood of the sub-area to be analyzed in the tth time period being a trend block. In this embodiment, the neighborhood size is eight neighborhoods.

[0059] The sub-region with the largest probability value in the neighborhood of the sub-region to be analyzed in the t-th time period is taken as the first trend block of the sub-region to be analyzed, the sub-region with the largest probability value in the neighborhood of the first trend block of the sub-region to be analyzed in the t+1-th time period is taken as the second trend block of the sub-region to be analyzed, and the sub-region with the largest probability value in the neighborhood of the second trend block of the sub-region to be analyzed in the t+2-th time period is taken as the third trend block of the sub-region to be analyzed, and so on, until the probability value is less than the preset probability threshold; when the probability value is small, it means that the smart connected vehicles in the sub-region to be analyzed in the t-th time period have dispersed or have reached the destination with the change of time, and there is no need to calculate the subsequent location information change trend. In this embodiment, the preset probability threshold is 0.33. In specific applications, the implementer can set it according to specific circumstances. All trend blocks of the sub-region to be analyzed constitute the query sensitive path corresponding to the sub-region to be analyzed in the t-th time period. Obtain the straight line segment between the starting point and the end point of the query sensitive path corresponding to the sub-region to be analyzed in the t-th time period, and take the closed area enclosed by the straight line segment and the query sensitive path as the query sensitive area corresponding to the sub-region to be analyzed, such as Figure 2 As shown in the figure, the query sensitive area is shown, where a in the figure is the query sensitive path, b in the figure is the straight line segment between the starting point and the ending point of the query sensitive path, and the closed area enclosed by a and b is the query sensitive area. It should be noted that the sub-areas where all points on a and b are located are sub-areas in the query sensitive area.

[0060] By adopting the above method, the query sensitive area corresponding to each sub-area in each time period can be obtained.

[0061] Step S3, according to the query sensitivity of each sub-area in each time period, obtain the number of false location information to be generated for each sub-area in each time period; combined with the area ratio of the query sensitive area corresponding to each sub-area in each time period and the number of false location information to be generated, determine the number of false location information to be generated in the inner area of ​​the query sensitive area and the number of false location information to be generated in the outer area of ​​each sub-area in each time period.

[0062] In the above steps, the query sensitivity of each sub-region in the target area corresponding to each time period has been determined. When generating the false location information of each sub-region in each time period, the distribution trend direction of the sub-region in the subsequent time period must be determined first. In the subsequent process of generating false location information, try to avoid generating false locations in the distribution trend direction to prevent illegal personnel from inferring the user's real location information based on the location information of sub-regions with the same distribution trend. Therefore, the correlation of location information between different sub-regions is first analyzed. For any sub-region, when calculating its false location information in the tth time period, first determine the trend direction of the possible distribution of the location information of the car in the sub-region in the subsequent time period.

[0063] Next, the sub-region to be analyzed and the t-th time period in the preset time interval are taken as an example for description. The method provided in this embodiment can be used to process other sub-regions and time periods.

[0064] In the above steps, this embodiment determines the query sensitive area corresponding to each sub-area in the target area in each time period, and then calculates the amount of pseudo location information that needs to be generated for each sub-area in each time period, wherein the higher the query sensitivity of each sub-area, the more pseudo location information that needs to be generated for the corresponding sub-area.

[0065] Specifically, for the t-th time period within the preset time interval: calculate the difference between the query sensitivity of the sub-area to be analyzed in the t-th time period and the minimum query sensitivity of all sub-areas in the t-th time period; obtain the adjustment coefficient by subtracting the constant 1 from the reciprocal of the difference; obtain the difference between the preset maximum value of the number of false location information to be generated and the preset minimum value of the number of false location information to be generated; calculate the rounded-down result of the product of the difference and the adjustment coefficient, and calculate the sum of the preset minimum value of the number of false location information to be generated and the rounded-down result; take the minimum value of the sum and the preset maximum value of the number of false location information to be generated as the number of false location information to be generated in the sub-area to be analyzed in the t-th time period. Among them, the preset maximum value of the number of false location information to be generated and the preset minimum value of the number of false location information to be generated are set by the implementer according to the specific situation.

[0066] In this embodiment, a specific calculation formula for the amount of false location information to be generated is given. The amount of false location information to be generated in the j-th sub-area in the t-th time period can be expressed as:

[0067]

[0068] Among them, F jt represents the number of false location information to be generated for the j-th sub-region in the t-th time period, F maxrepresents the maximum value of the number of false location information to be generated, F min represents the minimum value of the number of false location information to be generated, A jt A represents the query sensitivity of the jth sub-region in the tth time period, t,min represents the minimum query sensitivity of all sub-areas in the target area in the tth time period, λ1 represents the preset first adjustment parameter, and min() represents the minimum value function. Indicates the floor symbol.

[0069] In this embodiment, A jt ―A jt,min +λ1 is used to characterize the difference between the query sensitivity of the j-th sub-area in the t-th time period and the minimum query sensitivity of all sub-areas in the t-th time period. The preset first adjustment parameter is introduced into the calculation formula to prevent the denominator from being 0. In this embodiment, the preset first adjustment parameter is 0.01. In specific applications, the implementer can set it according to the specific situation. It represents the adjustment coefficient. The greater the difference between the query sensitivity of the j-th sub-area in the t-th time period and the minimum query sensitivity of all sub-areas in the t-th time period, the greater the number of false location information that needs to be generated for the corresponding j-th sub-area in the t-th time period. The maximum number of false location information of the j-th sub-area shall not exceed the maximum value of the preset number of false location information to be generated, so as to avoid excessive computing resources.

[0070] For each sub-area, there is a certain degree of correlation between its location information and the location information inside the query sensitive area. Therefore, when generating false location information, the false location information that needs to be generated inside the query sensitive area should be relatively small to avoid leaking the user's real location information. The area outside the query sensitive area needs to generate relatively more false location information. However, if the area occupied by the query sensitive area is larger than the area outside the query sensitive area, it means that the space inside the query sensitive area is large enough, and the generated false location information can be distributed discretely enough. Therefore, the amount of false location information that needs to be generated for the query sensitive area of ​​each sub-area is determined based on the area of ​​the query sensitive area and its external area.

[0071] For the t-th time period in the preset time area: the product of the area ratio of the query sensitive area corresponding to the sub-area to be analyzed in the t-th time period and the number of false location information to be generated in the sub-area to be analyzed in the t-th time period is rounded down to determine the number of false location information to be generated in the inner area of ​​the query sensitive area of ​​the sub-area to be analyzed in the t-th time period. The difference between the number of false location information to be generated in the sub-area to be analyzed in the t-th time period and the number of false location information to be generated in the inner area of ​​the query sensitive area of ​​the sub-area to be analyzed in the t-th time period is used as the number of false location information to be generated in the outer area of ​​the query sensitive area of ​​the sub-area to be analyzed in the t-th time period. Among them, the area ratio of the query sensitive area corresponding to the sub-area to be analyzed in the t-th time period is the ratio between the area of ​​the query sensitive area corresponding to the sub-area to be analyzed in the t-th time period and the total area of ​​the target area.

[0072] In this embodiment, a specific calculation formula for the amount of false location information to be generated in the internal area and the amount of false location information to be generated in the external area is given. The amount of false location information to be generated in the internal area and the amount of false location information to be generated in the external area of ​​the j-th sub-area in the t-th time period when querying the sensitive area can be expressed as:

[0073]

[0074] Q jt =F jt ―P jt

[0075] Among them, P jt represents the number of false location information to be generated in the inner area of ​​the sensitive area queried by the j-th sub-area in the t-th time period, Q jt represents the number of false location information to be generated when the j-th sub-region queries the external area of ​​the sensitive area in the t-th time period, F jt represents the number of false location information to be generated for the j-th sub-region in the t-th time period, M jt represents the area of ​​the query sensitive area corresponding to the jth sub-area in the tth time period, M represents the total area of ​​the target area, Indicates the floor symbol.

[0076] It is used to reflect the area ratio of the query sensitive area corresponding to the j-th sub-area in the t-time period. The larger the value is, the more false location information needs to be generated in the internal area of ​​the query sensitive area corresponding to the j-th sub-area in the t-time period, and the less false location information needs to be generated in its external area.

[0077] By adopting the above method, the amount of false location information to be generated in the inner area of ​​the sensitive area and the amount of false location information to be generated in the outer area of ​​the sensitive area can be obtained when each sub-area queries in each time period.

[0078] Step S4: Generate a corresponding set of false positions based on the amount of false position information to be generated in the internal area and the amount of false position information to be generated in the external area.

[0079] After determining the amount of false location information to be generated in the inner area and the amount of false location information to be generated in the outer area of ​​each sub-area in each time period to query the sensitive area, this embodiment will combine the k-anonymity algorithm to generate corresponding false location information to achieve the purpose of protecting the user's car privacy information.

[0080] Specifically, for each sub-region, the internal region and the external region of the sensitive region are queried in each time period, respectively, and the corresponding pseudo location information is generated using the k-anonymous algorithm, wherein the corresponding k value when each region uses the k-anonymous algorithm to select the pseudo location is the corresponding number of pseudo location information to be generated, that is, when the k-anonymous algorithm is used to select the pseudo location for the internal region of the query sensitive region, the corresponding k value is the number of pseudo location information to be generated for the internal region calculated above, and when the k-anonymous algorithm is used to select the pseudo location for the external region of the query sensitive region, the corresponding k value is the number of pseudo location information to be generated for the external region calculated above; the pseudo location information corresponding to the internal region of the query sensitive region and the pseudo location information corresponding to the external region of the query sensitive region for each sub-region in each time period together constitute a pseudo location set. The k-anonymous algorithm is a prior art, and will not be described in detail here.

[0081] When the user subsequently initiates a query request to the terminal server, the corresponding fake location set is first generated by the above method, which includes the user's real location. Then all the location information is returned to the user, and the user selects the corresponding result he wants based on his real location. When the illegal personnel need to query, because they do not know the user's real location, they cannot select the user's real location from multiple location sets.

[0082] This embodiment first determines the query sensitivity of each sub-area in each time period through the traffic information of the cars in each sub-area in the target area, and then considers that the position of the vehicle may change with the change of time, and there is a certain correlation between the position change of the car and the time change. Therefore, the corresponding query sensitive area is determined in combination with the change of the car VIN information in the neighborhood of each sub-area in the adjacent time period, and the number of false location information to be generated in each sub-area in each time period is determined according to the query sensitivity of each sub-area in each time period. At the same time, the number of false location information to be generated in the inner area of ​​the query sensitive area and the number of false location information to be generated in the outer area are determined according to the area ratio of the query sensitive area, thereby generating a false location set. Compared with the existing method, the number of false location information can be adaptively determined according to the query sensitivity of each sub-area in different time periods and the query sensitive path, thereby avoiding the occurrence of too much or too little false location information, improving the security of the car location privacy information of the intelligent network user, and reducing the risk of the car location information being leaked.

[0083] An embodiment of a personal privacy data security protection system for intelligent connected vehicles:

[0084] See also Figure 3 , which shows a structural block diagram of a personal privacy data security protection system for an intelligent connected vehicle provided by an embodiment of the present invention. The system includes a data acquisition module, a query sensitive area determination module, a false location information quantity determination module and a false location set generation module.

[0085] The data acquisition module is used to obtain the traffic information of cars in each sub-area of ​​the target area;

[0086] The query sensitive area determination module is used to determine the query sensitivity of each sub-area in each time period according to the traffic information of the cars in each sub-area; determine the query sensitive path corresponding to each sub-area in each time period in combination with the change of the car VIN information in the neighborhood of each sub-area in adjacent time periods; determine the query sensitive area based on the query sensitive path;

[0087] A module for determining the number of false location information is used to obtain the number of false location information to be generated for each sub-region in each time period according to the query sensitivity of each sub-region in each time period; combining the area ratio of the query sensitive region corresponding to each sub-region in each time period and the number of false location information to be generated, to determine the number of false location information to be generated in the inner area of ​​the query sensitive region and the number of false location information to be generated in the outer area of ​​each sub-region in each time period;

[0088] The false location set generation module is used to generate a corresponding false location set based on the amount of false location information to be generated in the internal area and the amount of false location information to be generated in the external area.

[0089] It should be understood that Figure 3 The structural block diagram of a smart connected car personal privacy data security protection system and its modules shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art can understand that the above methods and systems can be implemented using computer executable instructions and / or included in processor control codes, such as carrier media such as disks, CDs or DVD-ROMs, programmable memories such as read-only memories (firmware), or data carriers such as optical or electronic signal carriers. Such codes are provided on. The system and its modules of this specification can not only be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above hardware circuits and software (for example, firmware).

[0090] For more details about the above modules, please refer to other places in this manual and will not be repeated here.

[0091] In other embodiments, a device is also provided, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the device executes the above-mentioned intelligent classification and identification method of electrical equipment based on feature analysis. The device can be specifically a chip, a component or a module, and the chip may include a connected processor and a memory; wherein the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute a method for protecting personal privacy data of an intelligent connected car provided in the above-mentioned embodiment.

[0092] In other embodiments, a computer program product is also provided. When the computer program product is run on a computer, the computer executes the above-mentioned related steps to implement a method for securely protecting personal privacy data of an intelligent connected vehicle provided in the above-mentioned embodiment.

[0093] In other embodiments, a computer-readable storage medium is also provided, in which a computer program code is stored. When the computer program code is run on a computer, the computer executes the above-mentioned related method steps to implement a method for securely protecting personal privacy data of an intelligent connected vehicle provided in the above-mentioned embodiment.

[0094] Among them, the provided systems, devices, computer program products, and computer-readable storage media are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding methods provided above and will not be repeated here.

[0095] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for protecting personal privacy data security of an intelligent connected vehicle, characterized in that: The method comprises the following steps: Obtain the traffic information of cars in each sub-area in the target area; Determine the query sensitivity of each sub-area in each time period based on the traffic information of the cars in each sub-area; determine the query sensitive path corresponding to each sub-area in each time period based on the change of the car VIN information in the neighborhood of each sub-area in adjacent time periods; determine the query sensitive area based on the query sensitive path; According to the query sensitivity of each sub-region in each time period, the number of false location information to be generated for each sub-region in each time period is obtained; based on the area ratio of the query sensitive area corresponding to each sub-region in each time period and the number of false location information to be generated, the number of false location information to be generated in the inner area and the number of false location information to be generated in the outer area of ​​the query sensitive area of ​​each sub-region in each time period is determined; Generate a corresponding set of false positions based on the amount of false position information to be generated in the internal area and the amount of false position information to be generated in the external area; The acquisition of the amount of false location information to be generated in each sub-area in each time period includes: Calculate the difference between the query sensitivity of the sub-area to be analyzed in the tth time period and the minimum query sensitivity of all sub-areas in the tth time period; subtract a constant 1 from the reciprocal of the difference to obtain an adjustment coefficient; Obtaining a difference between a preset maximum value of the number of false location information to be generated and a preset minimum value of the number of false location information to be generated; calculating a rounding-down result of the product of the difference and the adjustment coefficient, and calculating a sum of a preset minimum value of the number of false location information to be generated and the rounding-down result; The minimum value between the sum and the maximum value of the preset number of false location information to be generated is used as the number of false location information to be generated in the sub-area to be analyzed in the tth time period; The sub-region to be analyzed is any sub-region in the target region.

2. A method for protecting personal privacy data of an intelligent connected vehicle according to claim 1, characterized in that: The step of determining the query sensitivity of each sub-area in each time period according to the traffic information of the cars in each sub-area includes: The ratio between the number of times that the positions of all vehicles sent to the terminal via the vehicle GPS in the candidate time period are the sub-area to be analyzed and the total number of times that the positions of all vehicles sent to the terminal via the vehicle GPS in the preset time interval are the sub-area to be analyzed is used as the query sensitivity of the sub-area to be analyzed in the candidate time period; The candidate time period is any time period within a preset time interval.

3. A method for protecting personal privacy data of an intelligent connected vehicle according to claim 2, characterized in that: The step of combining the changes in the automobile VIN information in the neighborhood of each sub-area in adjacent time periods to determine the query sensitive path corresponding to each sub-area in each time period includes: According to the relationship between the number of VIN information contained in each time period of the sub-region to be analyzed and the number of VIN information contained in the next time period corresponding to each sub-region in the neighborhood of the sub-region to be analyzed, the possibility value of each sub-region in the neighborhood of the sub-region to be analyzed in each time period being a trend block is obtained; The possibility value is used to screen the query sensitive paths corresponding to the sub-area to be analyzed in the candidate time period.

4. A method for protecting personal privacy data of an intelligent connected vehicle according to claim 3, characterized in that: The method of obtaining the possibility value of each sub-region in the neighborhood of the sub-region to be analyzed in each time period being a trend block according to the relationship between the amount of VIN information contained in each time period of the sub-region to be analyzed and the amount of VIN information contained in the next time period corresponding to each sub-region in the neighborhood of the sub-region to be analyzed includes: The ratio between the number of types of VIN information contained in the t+1th time period of each sub-region in the neighborhood of the sub-region to be analyzed and the number of types of VIN information contained in the tth time period of the sub-region to be analyzed is taken as the possibility value that the corresponding sub-region in the neighborhood of the sub-region to be analyzed in the tth time period is a trend block.

5. A method for protecting personal privacy data of an intelligent connected vehicle according to claim 3, characterized in that: The query sensitive paths corresponding to the sub-area to be analyzed in each time period are screened using the possibility value, including: The sub-region with the largest possibility value in the neighborhood of the sub-region to be analyzed in the t-th time period is taken as the first trend block of the sub-region to be analyzed, the sub-region with the largest possibility value in the neighborhood of the first trend block of the sub-region to be analyzed in the t+1-th time period is taken as the second trend block of the sub-region to be analyzed, the sub-region with the largest possibility value in the neighborhood of the second trend block of the sub-region to be analyzed in the t+2-th time period is taken as the third trend block of the sub-region to be analyzed, and so on, until the possibility value is less than the preset possibility threshold; All trend blocks of the sub-region to be analyzed constitute the query sensitive path corresponding to the sub-region to be analyzed in the tth time period.

6. A method for protecting personal privacy data of an intelligent connected vehicle according to claim 5, characterized in that: The determining the query sensitive area based on the query sensitive path includes: A straight line segment between a starting point and an ending point of a query sensitive path corresponding to the sub-region to be analyzed in the tth time period is obtained, and a closed area enclosed by the straight line segment and the query sensitive path is used as a query sensitive area corresponding to the sub-region to be analyzed.

7. The method for protecting personal privacy data of an intelligent connected vehicle according to claim 1, characterized in that: The determining the amount of false location information to be generated in the inner area and the outer area of ​​the query sensitive area of ​​each sub-area in each time period in combination with the area ratio of the query sensitive area corresponding to each sub-area in each time period and the amount of false location information to be generated includes: The product of the area ratio of the query sensitive area corresponding to the sub-area to be analyzed in the tth time period and the amount of false location information to be generated in the sub-area to be analyzed in the tth time period is rounded down to determine the amount of false location information to be generated in the inner area of ​​the query sensitive area of ​​the sub-area to be analyzed in the tth time period; The difference between the amount of false location information to be generated in the sub-region to be analyzed in the t time period and the amount of false location information to be generated in the inner area of ​​the sensitive area queried by the sub-region to be analyzed in the t time period is used as the amount of false location information to be generated in the outer area of ​​the sensitive area queried by the sub-region to be analyzed in the t time period; The area ratio of the query sensitive area corresponding to the sub-area to be analyzed in the t time period is the ratio between the area of ​​the query sensitive area corresponding to the sub-area to be analyzed in the t time period and the total area of ​​the target area.

8. The method for protecting personal privacy data of an intelligent connected vehicle according to claim 1, characterized in that: The generating a corresponding false location set based on the amount of false location information to be generated in the internal area and the amount of false location information to be generated in the external area includes: For each sub-region, the inner region and the outer region of the sensitive region are queried in each time period, and the corresponding false location information is generated using the k-anonymity algorithm, wherein the corresponding k value when each sub-region uses the k-anonymity algorithm to select the false location is the corresponding number of false location information to be generated; Each sub-region queries the pseudo location information corresponding to the inner region and the pseudo location information corresponding to the outer region of the sensitive region in each time period, which together constitute a pseudo location set.

9. A personal privacy data security protection system for an intelligent connected vehicle, the system being used to execute the method of claim 1, characterized in that: The system includes: A data acquisition module is used to obtain the traffic information of cars in each sub-area in the target area; The query sensitive area determination module is used to determine the query sensitivity of each sub-area in each time period according to the traffic information of the cars in each sub-area; determine the query sensitive path corresponding to each sub-area in each time period in combination with the change of the car VIN information in the neighborhood of each sub-area in adjacent time periods; determine the query sensitive area based on the query sensitive path; A module for determining the number of false location information is used to obtain the number of false location information to be generated for each sub-region in each time period according to the query sensitivity of each sub-region in each time period; combining the area ratio of the query sensitive region corresponding to each sub-region in each time period and the number of false location information to be generated, to determine the number of false location information to be generated in the inner area of ​​the query sensitive region and the number of false location information to be generated in the outer area of ​​each sub-region in each time period; The false location set generation module is used to generate a corresponding false location set based on the amount of false location information to be generated in the internal area and the amount of false location information to be generated in the external area.

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