CN113051357A - Vector map optimization local desensitization method based on game theory - Google Patents

Vector map optimization local desensitization method based on game theory Download PDF

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CN113051357A
CN113051357A CN202110249302.2A CN202110249302A CN113051357A CN 113051357 A CN113051357 A CN 113051357A CN 202110249302 A CN202110249302 A CN 202110249302A CN 113051357 A CN113051357 A CN 113051357A
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宋军
杨帆
余垚
徐衡
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Abstract

The invention provides a vector map optimized local desensitization method based on a game theory, which comprises the following steps: acquiring vector map data to be desensitized, extracting sensitive information of the vector map data and analyzing the content of the sensitive information; constructing a sensitive information protection model based on a game theory, evaluating the behaviors of all participants in a sensitive information protection scene of a vector map, calculating the profits of the participants and carrying out strategy analysis; aiming at given data of different participants, giving consideration to the requirements of data security protection and availability, and making an optimal decision; selecting desensitization geographic elements, and completing local desensitization of the vector map by using the generated chaotic sequence in combination with a sensitive information protection model and an optimal desensitization algorithm; and outputting the map data result after local desensitization is completed.

Description

Vector map optimization local desensitization method based on game theory
Technical Field
The invention relates to the field of vector map desensitization, in particular to a vector map optimized local desensitization method based on a game theory.
Background
The vector digital map is a carrier of geographic information, is a basic information resource for implementing ecological environment protection, disaster prediction and early warning, geographic space information sharing and natural resource information integration, and is also an indispensable strategic resource in national economy and national defense construction. As an important part of geographic information sharing, the information security and open sharing problem of vector digital maps has become a key problem in the research challenge of current geographic information services. In order to better deal with the threat of malicious behaviors such as stealing, tampering, counterfeiting, infringement, disclosure and the like to the safety of the vector map data, the sensitive data protection method proposed by the existing research can better guarantee the safety of the vector map data in storage, transmission, display and output, but the open sharing and the high-efficiency availability of the vector map data are not fully considered.
In the process of implementing the present invention, the inventor has gathered related patents known at present, and finds that the methods for protecting vector map data, which are common at present, can be mainly divided into a digital watermarking method and a data scrambling method. A digital vector map integrity protection method (CN 201210189469.5) proposed by Harbin engineering university relates to a method for completely protecting and authenticating a digital vector map, which can resist various attacks and improve the data writing speed; an asymmetric watermark method [ CN201410241263.1 ] for protecting vector map data copyright proposed by Yanghe et al solves the problem of embedding and detecting asymmetric watermark and eliminating secret key channel distribution key; the vector space data encryption method using Haar transform and gaussian distribution [ CN202010328668.4 ] proposed by Yan Haonwien et al relates to a vector space data encryption method using tree-shaped Haar transform and gaussian distribution. The method only considers how to protect the vector map data to the maximum extent, but does not consider the availability of the vector map data, the demand of shared service and desensitized controllability.
Disclosure of Invention
Aiming at the defects, the invention provides a vector map optimization local desensitization method based on the game theory, which can effectively protect sensitive information in the vector map and is also suitable for other fields of geological and geographic information sharing protection.
The invention provides a vector map optimized local desensitization method based on a game theory, which specifically comprises the following steps:
s101, obtaining vector map data to be desensitized, extracting sensitive information of the vector map data and analyzing the content of the sensitive information;
s102, constructing a sensitive information protection model based on a game theory, evaluating the behaviors of all participants in a sensitive information protection scene of a vector map, calculating the profits of the participants and carrying out strategy analysis;
s103, aiming at given data of different participants, giving consideration to data security protection and usability requirements, and making an optimal decision;
s104, selecting desensitization geographic elements, and completing local desensitization of the vector map by using the generated chaotic sequence in combination with a sensitive information protection model and an optimal desensitization algorithm;
and S105, outputting the map data result after the local desensitization is finished.
Further, step S101 specifically includes:
the vector map data to be desensitized is composed of a plurality of map layers, and specifically comprises the use condition of the sensitive geographic element, the relevance with other surrounding sensitive geographic elements and the weight occupied by the sensitive geographic element.
Further, the total content of sensitive information of the vector map is shown as formula (1):
Figure RE-GDA0003036964150000021
infr in formula (1)mapRepresenting the total content of sensitive information of the vector map; i is the number of the geographic elements in the map layer; j is the layer number; n is the total number of layers; m is the total number of geographic elements in one map layer; weightiWeight occupied by sensitive geographic elements; stateiA usage status for a sensitive geographic element; localityiTo a degree of closeness of association with other sensitive surrounding geographic elements.
Further, in step S102, the participants include: data defender DpAnd data attacker Da。
Further, data defender DpThe policy space of (a) is expressed as: sp＝(sp1,sp2…spm)；
Data attacker DaThe policy space of (a) is expressed as: sa＝(sa1,sa2…san)；
Data defender DpThe yield of (a) is expressed by formula (2):
Figure 1
in the formula (2), the reaction mixture is,
Figure RE-GDA0003036964150000032
is a data defender DpThe benefit of sensitive information resulting from implementing a desensitization strategy,
Figure RE-GDA0003036964150000033
is a data defender DpLoss of shared data in the vector map caused by implementation of a desensitization policy; forsecRepresenting the total amount of sensitivity important information; forunsecRepresenting the total amount of sharing important information; r represents a weighting factor; saiAs a policy space SaAny one of the above; spiAs a policy space SpAny one of the above;
data attacker DaThe yield of (a) is expressed by the formula (3):
Figure 2
in the formula (3), the reaction mixture is,
Figure RE-GDA0003036964150000035
representing data aggressors DaSensitive information revenue obtained by executing the corresponding neutralization strategy;
Figure RE-GDA0003036964150000036
representing data aggressors DaData defender D when executing certain sensitive information attack methodpFor data attacker DaThe resulting loss of trust;
the participants, the policy space and the revenue function together form a quadruple model G ═ (Sp, Sa, up, ua).
Step S104 specifically includes:
s201: combining the composition characteristics of vector map elements, and generating a random noise set sequence by adopting a Henon two-dimensional chaotic system;
s202: and selecting the geographic elements needing to perform desensitization operation, and performing element-oriented deletion, offset, replacement, scrambling desensitization operation according to interference data in the random noise set sequence to complete local desensitization of the vector map.
The beneficial effects provided by the invention are as follows: the safety and sharing requirements of different vector maps are better considered, and the local desensitization of the vector maps is effectively realized; the method can be suitable for the vector map data desensitization requirements in the aspects of data sharing, safe transmission, encapsulation storage and the like, can support the sensitive information protection of important data such as geology, geography, hydrology and the like, and effectively solves the contradiction problem between the map information security and confidentiality and the open sharing.
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FIG. 1 is a flow chart of a vector map optimized partial desensitization scheme based on game theory;
FIG. 2 is a schematic diagram of a vector map desensitization scheme;
FIG. 3 is raw vector map data;
FIG. 4 shows a data publisher selecting geographic elements requiring local desensitization from a vector map according to a revenue calculation result;
FIG. 5 is a schematic diagram of the change in geographic elements before and after performing linear local desensitization based on chaotic sequences;
FIG. 6 is a comparison of the effect of a vector map before and after desensitization;
FIG. 7 is the effect of vector maps after desensitization;
FIG. 8 is a depiction of the associated graphical symbol of FIGS. 3-7;
FIG. 9 is a schematic diagram of the desensitization effect of the vector map-based geographic elements when the perturbation factor θ takes a value of 0.0001;
FIG. 10 is a schematic diagram of the desensitization effect of the vector map surface-type geographic elements when the perturbation factor θ takes a value of 0.00005;
FIG. 11 is a schematic diagram showing significant distortion and misalignment of the spatial positions of geographic elements in a vector map.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be further described with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a flowchart of a vector map optimization local desensitization method based on game theory according to the present invention; a vector map optimization local desensitization method based on game theory comprises the following steps:
s101, obtaining vector map data to be desensitized, extracting sensitive information of the vector map data and analyzing the content of the sensitive information;
the vector map data to be desensitized is composed of a plurality of map layers, and specifically comprises the use condition of the sensitive geographic element, the relevance with other surrounding sensitive geographic elements and the weight occupied by the sensitive geographic element.
Vector maps are generally organized in multiple levels, such as geographic elements, geographic map layers, and vector maps. For a particular map Layer { l1, l2 … lm } (1< i < ═ m), where li represents a geographic element in the Layer and m is the number of geographic elements contained in this map Layer. For example, one copper ore information map layer may be represented by m points. In practical applications, the sensitive information content of the map layer varies according to the time, space, weight and number of geographic elements li.
Therefore, the sensitive information content defining a geographical map layer is as follows:
Figure RE-GDA0003036964150000051
wherein, statei represents the use state of the sensitive geographic element, such as in use, standby, abandonment, etc.; here, the value range of statei is set to be [0-100], and the value of statei is in direct proportion to the sensitive information content Inforlayer of the geographical layer; the localyi represents the closeness degree of the relevance with other surrounding sensitive geographic elements, the value range is [0-100], and the localyi is in direct proportion to Inforiayer; weight i represents the weight taken up by the sensitive geographic element.
1. Then, for a plurality of image layers, the total content of sensitive information of the vector map is as shown in formula (1):
Figure RE-GDA0003036964150000061
in formula (1), Informap represents the total content of sensitive information of the vector map; i is the number of the geographic elements in the map layer; j is the layer number; n is the total number of layers; m is the total number of geographic elements in one map layer;
s102, constructing a sensitive information protection model based on a game theory, evaluating the behaviors of all participants in a sensitive information protection scene of a vector map, calculating the profits of the participants and carrying out strategy analysis;
s103, aiming at given data of different participants, giving consideration to data security protection and usability requirements, and making an optimal decision;
referring to fig. 3, fig. 3 is a block diagram of a vector map sensitive information protection scheme.
The sensitive information protection scheme mainly comprises three processes, which are respectively as follows: based on the selection of game theory desensitization geographic elements, the local desensitization of the vector map and the desensitization reduction of the vector map.
The protection scheme involves two participants: data defender Dp, i.e. data publisher; data attacker Da, i.e. data consumer. Revenue u for participant Dpp(spi，sai) Can be expressed as:
Figure RE-GDA0003036964150000062
wherein the content of the first and second substances,
Figure RE-GDA0003036964150000063
is participant DpThe benefit of sensitive information resulting from implementing a desensitization strategy,
Figure RE-GDA0003036964150000064
is participant DpLoss of shared data in the vector map caused by implementing a desensitization strategy.
Combining the participants, the strategy space and the gain function to form a four-tuple model G ═ (Sp, Sa, u)p,ua) The Best Response policy for participant i is defined as follows:
Figure RE-GDA0003036964150000065
a strategy is provided by each participant, and the strategies form a strategy set (S)p，Sa). For one pair of strategy combination
Figure RE-GDA0003036964150000071
Policy for any participant { i | i \ in { p, a }
Figure RE-GDA0003036964150000072
All for other participant policy combinations
Figure RE-GDA0003036964150000073
The best countermeasure of (1). If it is not
Figure RE-GDA0003036964150000074
For arbitrary st∈SiAre all established, then
Figure RE-GDA0003036964150000075
Is a "Nash Equilibrium" (Nash Equilibrium) of the sensitive information protection model G;
data publisher DpCapable of using a given data set DaStrategy s ofaiAnd respectively obtaining the optimal data desensitization scheme of the vector map. Thus, data consumer DaCan be expressed as
Figure RE-GDA0003036964150000076
When data publisher DpAnd data user DaWhen the optimal strategy is selected simultaneously, the vector digital sensitive information protection model gradually approaches a stable state, and the nash equilibrium state can be expressed as follows:
Figure RE-GDA0003036964150000077
s104, selecting geographical elements needing desensitization, and completing local desensitization of the vector map by using the generated chaotic sequence in combination with a sensitive information protection model and an optimal desensitization algorithm;
referring to FIG. 2, in order to provide effective desensitization noise, the five-tuple [ a, b, x ] of Henon two-dimensional chaotic parameters is used in combination with the structural features of vector map elements0,y0,T0]Generating a random noise set sequence, wherein a and b are control parameters and x0,y0Is an initial value of a random noise generator, T0Is the initial number of iterations. For the initial value x0,y0Iteration T0Then, x can be obtainedn,yn. Subsequent iterations m-1 more times can produce a random noise sequence of length m:
Figure RE-GDA0003036964150000078
the parameter quintuple is a reconstructed chaotic sequence generator and is also a necessary condition for realizing the desensitization data of the vector map to be restored to an original state. In the present invention, the corresponding parameter five-tuple { a, b, x0,y0,T0Will be used as the restoration key for the vector map desensitization data. According to the Kerkhoffs criterion, even the open chaos sequenceAnd (4) a column generation algorithm cannot restore the desensitized vector map to the original map state if the secret key is unknown.
Based on the game income, selecting the geographic elements which need to execute desensitization operation in the given vector map, generating interference data according to the two-dimensional chaotic sequence, and executing desensitization operation on the selected geographic elements. Given a data disturber D for any vector map containing sensitive informationpSelected policy space SpThe digital map local desensitization scheme should satisfy the following formula (0, 10%, 20%, …, 100%):
Figure RE-GDA0003036964150000081
wherein the content of the first and second substances,
Figure RE-GDA0003036964150000082
and
Figure RE-GDA0003036964150000083
expressed as max and s, respectivelypiAnd n is the content of sensitive information of the geographic elements. And finally, local desensitization of the vector map is completed by combining the sensitive information protection model and the chaotic sequence generation method.
While the second step of fig. 2 also illustrates several different methods of local desensitization of data used by the present invention, including scrambling, shifting, replacing, and deleting certain map elements.
Finally, the desensitization reduction algorithm of the vector map data is the inverse process of the desensitization algorithm. Namely, given a desensitized vector map, the original vector map can be obtained through desensitization reduction operation. It should be noted that, as can be seen from the sensitive information content calculation method, the sensitive information content of each geographic element does not change due to the desensitization operation.
And S105, outputting the map data result after the local desensitization is finished.
Referring to fig. 3-7, to better embody the local desensitization effect of the vector map data in the present embodiment, we select an applicationAnd (5) carrying out experimental verification and analysis on the scene. Wherein, the data user D is setpSelection of sa40% r 0.6(1-r 0.4), data distributor DpThe desensitization strategy chosen is sp17.5%, the original vector map in fig. 3 and 7 is subjected to linear perturbation based on a chaotic sequence, and the vector map effects before and after local desensitization are shown in fig. 3-7.
As shown in fig. 3-7, fig. 3 is raw vector map data; FIG. 4 shows a data publisher selecting geographic elements requiring local desensitization from a vector map according to a revenue calculation result; FIG. 5 illustrates the change in geographic elements before and after performing linear local desensitization based on chaotic sequences; fig. 6 shows the effect of the vector map before and after desensitization, and fig. 7 shows the effect of the vector map after desensitization. As can be seen from fig. 3 to fig. 7, the vector map partial desensitization scheme based on the game theory proposed in the present invention can pick the optimal geographic element data set based on the profits of game participants, as shown in fig. 5 and fig. 6. And further performing local coordinate linear disturbance based on the chaotic sequence on the geographic elements in the selected vector map, thereby achieving the desensitization purpose of reducing the geometric precision of the geographic elements, as shown in fig. 4 and 7.
As shown in fig. 3-7, the overall effect of local desensitization of the vector map execution point, line, and surface geographic elements is shown. According to the scheme, the point, line and surface local sensitive map elements in the vector map can be effectively selected, and overall linear offset is carried out according to the algorithm, so that the purposes of reducing the geometric precision and maintaining the stability of the geometric shape of the map elements can be achieved. This further illustrates the better topological conformality of the proposed solution. FIG. 8 is a depiction of the associated graphical symbol of FIGS. 3-7; special points are characteristic points; buildings are Buildings; highways is a road; grassland is Grassland; naturals is the greenbelt; railways is a railroad;
as shown in fig. 9-11, the adjustment of the coordinate interference degree in the corresponding geographic element can be realized by different settings of the perturbation factor θ, and the results of the desensitization experiments based on different perturbation factors are shown in fig. 9-11.
Fig. 9 and 10 show the desensitization effect of the vector map surface-type geographic element when the perturbation factor theta takes values of 0.0001 and 0.00005 respectively. From the experimental result, when the value of the disturbance factor theta is large, the position coordinate change of the geographic element is also large, so that the sensitive geographic data can be more effectively protected. However, as the value of the perturbation factor θ is increased, it can be seen from fig. 11 that the spatial position of the geographic element in the vector map will exhibit obvious distortion and misalignment (i.e. the distortion and misalignment cause overlap), and the spatial relationship causes serious damage. Therefore, in the process of implementing local desensitization of the vector map, a data publisher should consider fully combining with practical application scenarios to select disturbance factors with proper sizes.
The beneficial effects provided by the invention are as follows: the invention provides a novel vector map optimized local desensitization scheme based on a game theory aiming at the problems of safety protection and open sharing of sensitive information of a vector map. Establishing a sensitive information protection model and an optimal data desensitization model by introducing a game theory, wherein a defender sets and executes a sensitive data protection strategy; and the attacker has the ability to recover and acquire sensitive data information in the vector map. On the basis, by designing a sensitive data protection mechanism and a participant profit and cost evaluation model, comprehensively considering profits and costs of competing parties under different game decisions, and taking the paid cost and the obtained benefit for executing sensitive information protection as key factors to be considered and balanced when a map defender makes a decision, the rational selection process of each participant is simulated. And finally, constructing a vector map optimized local desensitization scheme applicable to different application scenes according to a reaction function and a Nash equilibrium principle. Meanwhile, the desensitization protection scheme provided by the invention can also be applied to other fields of geological and geographic information sharing protection. The vector map desensitization method is based on the OpenStreetMap real map scene, and the implementation efficiency of the vector map desensitization scheme and the controllability of data interference are analyzed and verified.
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, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (7)

1. A vector map optimization local desensitization method based on game theory is characterized in that: the method specifically comprises the following steps:
s101, obtaining vector map data to be desensitized, extracting sensitive information of the vector map data and analyzing the content of the sensitive information;
s102, constructing an important information protection model based on a game theory, evaluating the behaviors of all participants in an important information protection scene of a vector map, calculating the profits of the participants and carrying out strategy analysis;
s103, aiming at given data of different participants, giving consideration to data security protection and usability requirements, and making an optimal decision;
s104, selecting geographical elements needing desensitization, and completing local desensitization of the vector map by using the generated chaotic sequence in combination with a sensitive information protection model and an optimal desensitization algorithm;
and S105, outputting the map data result after the local desensitization is finished.
2. A method of game theory based vector map optimized local desensitization according to claim 1, wherein: step S101 specifically includes:
the vector map data to be desensitized is composed of a plurality of map layers, and specifically comprises the use condition of the sensitive geographic element, the relevance with other surrounding sensitive geographic elements and the weight occupied by the sensitive geographic element.
3. A method of game theory based vector map optimized local desensitization according to claim 2, wherein: the total content of sensitive information of the vector map is shown as formula (1):
Figure RE-FDA0003036964140000011
in the formula (1)，InformapRepresenting the total content of sensitive information of the vector map; i is the number of the geographic elements in the map layer; j is the layer number; n is the total number of layers; m is the total number of geographic elements in one map layer; weightiWeight occupied by sensitive geographic elements; stateiA usage status for a sensitive geographic element; localityiTo a degree of closeness of association with other sensitive surrounding geographic elements.
4. A method of game theory based vector map optimized local desensitization according to claim 1, wherein: in step S102, the participants include: data defender DpAnd data attacker Da。
5. A method of game theory based vector map optimized local desensitization according to claim 4, wherein:
data defender DpThe policy space of (a) is expressed as: sp＝(sp1,sp2…spm)；
Data attacker DaThe policy space of (a) is expressed as: sa＝(sa1,sa2…san)；
Data defender DpThe yield of (a) is expressed by formula (2):
Figure RE-FDA0003036964140000021
in the formula (2), the reaction mixture is,
Figure RE-FDA0003036964140000022
is a data defender DpThe benefit of sensitive information resulting from implementing a desensitization strategy,
Figure RE-FDA0003036964140000023
is a data defender DpLoss of shared data in the vector map caused by implementation of a desensitization policy; forsecIndicating high sensitivityTotal amount of information to be transmitted; forunsecRepresenting the total amount of sharing important information; r represents a weighting factor; saiAs a policy space SaAny one of the above; spiAs a policy space SpAny one of the above;
data attacker DaThe yield of (a) is expressed by the formula (3):
Figure RE-FDA0003036964140000024
in the formula (3), the reaction mixture is,
Figure RE-FDA0003036964140000025
representing data aggressors DaSensitive information revenue obtained by executing the corresponding neutralization strategy;
Figure RE-FDA0003036964140000026
representing data aggressors DaData defender D when executing certain sensitive information attack methodpFor data attacker DaResulting in a loss of trust. The participants, the strategy space and the revenue function together form a four-tuple model G ═ (S)p,Sa,up,ua)。
6. A method of game theory based vector map optimized local desensitization according to claim 5, wherein: step S103 specifically includes: while data defender DpAnd data attacker DaAnd do so simultaneously.
And (3) when the optimal decision is made, the sensitive information protection model reaches a Nash equilibrium state as shown in the following formula (4):
Figure RE-FDA0003036964140000031
wherein S pi and S ai are the optimal strategy for the data defender Dp and the optimal strategy for the data attacker Da, respectively.
7. A method of game theory based vector map optimized local desensitization according to claim 1, wherein: step S104 specifically includes:
s201: combining the composition characteristics of vector map elements, and generating a random noise set sequence by adopting a Henon two-dimensional chaotic system;
s202: and selecting the geographic elements needing to perform desensitization operation, and performing element-oriented deletion, offset, replacement, scrambling desensitization operation according to interference data in the random noise set sequence to complete local desensitization of the vector map.
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