CN105975871B - A kind of protecting sensitive data method and system - Google Patents
A kind of protecting sensitive data method and system Download PDFInfo
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Abstract
The disclosure discloses a kind of protecting sensitive data method and system; applied to Intelligent mobile equipment; methods described includes the tense related information of statistics perception data correspondence context; context sensitivity set, the risk threshold value of context sensitivity leakage are set; based on the tense related information, context sensitivity set, risk threshold value, the submission probability of current perception data is calculated, threshold value is submitted in random generation; the submission probability obtained according to calculating, selection perception data carries out the step such as submitting；Methods described considers the disclosure risk that current perception data is triggered to previous moment and the possible sensitive data of later moment in time, decides whether to submit current perception data by calculating, to ensure the disclosure risk of user's sensitive data in controlled range；Meanwhile, local tense related information of the methods described when submitting perception data only only in accordance with current perception data, required computing resource and storage resource is smaller, is more suitable for realizing on Intelligent mobile equipment.
Description
Technical field
This disclosure relates to data security arts, particularly a kind of protecting sensitive data method and system.
Background technology
At present, various sensors (such as microphone, camera, GPS, gyroscope, acceleration, optical sensor) be embedded in
Intelligent mobile equipment (such as smart mobile phone, mobile flat board, intelligent watch, Intelligent bracelet) progressively incorporate the daily of people
In Working Life.Meanwhile, the application (application, abbreviation app) that may operate on Intelligent mobile equipment is largely developed
Out, including context-aware applications (Context-aware applications).Context-aware applications can be according to
The perception data that embedded sensor is gathered in the Intelligent mobile equipment carried according to user, is inferred to the current context of user
Information, so as to provide the user personalization, context-aware service.Such as, with reference to GPS (global positioning system, Global
Positioning System) information that sensor is gathered, the position (being in or in office etc.) of user can be pushed off
Out, the data that acceleration transducer is gathered may infer that user motion state (it is static, walk or by bus etc.), microphone
(Mic) intensity of sound (whether there is noise) around user can be obtained.At present, it is widely used on Intelligent mobile equipment
Include than more typical context-aware applications：Wechat (knowing wechat user around user by shaking function),
Whether Twitter (can be to the other users pushed information for around using Twitter), GeoReminder (can inform user
Reach specified location), intelligent health bracelet (user's amount of exercise of one day can be detected).
On the other hand, the context-aware applications based on Intelligent mobile equipment platform are giving people to provide context-sensitive
In service process, the risk for also having triggered serious user's sensitive data to reveal.Such as, personal sensitive data (such as suffers from certain
Disease, it is in Special geographical position) it may be inferred to by the context-aware applications of malice, and illegally it is sold to third party's clothes
Be engaged in device or advertiser, to obtain economic interests or other interests.If a malice clique knows that user's is quick by malice app
Feel positional information (such as user is current to walk alone on remote road), then user may be by serious life wealth
Production is threatened.But, it is contemplated that the convenient personalized service that context-aware applications are provided, most of users may also can
It is continuing with these context-aware applications.Therefore, how research effectively protects user's quick on Intelligent mobile equipment platform
Contextual information is felt, with important practical significance and wide application prospect.
From existing achievement in research, the current protecting sensitive data method and system institute towards Intelligent mobile equipment
The granularity of user's sensitive data of protection is thicker, such as in Android (Android) and apple (IOS) system, user often only
It can statically specify an app to use the device resources such as particular sensor, and in fine granularity app can not be specified dynamically to make
With sensor, such as, as long as user is in hospital, Baidu map app cannot access gps data, so as to protect the position of user
Privacy-sensitive data.But, simple dynamic protecting sensitive data strategy can not effectively protect the sensitive number of user sometimes
According to.Under this policy, if user is not presently within context sensitivity, then allow app to access the contextual information；Otherwise prohibit
Only it is accessed.The main cause for causing the dynamic strategy to fail is there is tense related information between the contextual information of user.
Such as, the florist's shop of user currently near hospital, then following user goes the possibility of hospital just than larger.One attacker
The tense related information between context can be utilized, is inferred to whether user is currently in above and below certain sensitivity with greater probability
Text.
Therefore, on the basis of context tense related information is taken into full account, design is a kind of to be applied to Intelligent mobile equipment
Fine-grained dynamic protecting sensitive data method, be very necessary.
The content of the invention
In view of the above-mentioned problems, present disclose provides a kind of protecting sensitive data method, methods described comprises the steps：
S100, statistics perception data correspondence context tense related information；
S200, the risk threshold value for setting context sensitivity set, context sensitivity to reveal；
S300, based on the tense related information, context sensitivity set, risk threshold value, calculate current perception data
Submit probability；
Threshold value is submitted in S400, random generation, and the submission probability obtained according to calculating, selection perception data is submitted.
Further, the tense related information of perception data is counted in the step S100, is comprised the steps：
S101, one period set T={ t of acquisition1, t2... } represent, wherein, tiFor the i-th period, i=1,
2 ..., N, N are the sum of period；
S102, the various contexts being likely to occur to each period, deduction on the period, with set CiDuring expression
Between section tiOn the various contexts that are likely to occur；And the probability that each context occurs on each period is counted, useMark
Know in time period tiOn go out occurrence context c probability, wherein c ∈ Ci, i=1,2 ..., N；
S103, for two adjacent periods, count from a context transfer of previous time period to latter time
The probability of another context of section；WithRepresent in time period tiUnder conditions of place context c, in next time period ti+1Will
Probability in context c ', wherein c, c ' ∈ Ci, i=1,2 ..., N-1.
Preferably, probability is submitted to be calculated by following formula in the step S300：
And following condition is set up,
In formula：
ciIt is user in time period tiThe set of context being likely to be at；S is the context sensitivity set that user is set, i=
2,3 ..., N-1；
pC, cIt is the probability that the corresponding perception datas of context c are submitted when user is in context c；pC ', cIt is to work as user
The probability of the data corresponding to context c is submitted when being in context c ', wherein, c ' ≠ c；
δ is the risk threshold value for the context sensitivity leakage that user is set；
It is user in time period tiIt is in context c probability；It is user in time period ti-1It is in context c '
Probability,It is user in time period ti+1It is in context c " probability；It is user in time period tiIt is in context c '
Probability；
It is user in time period tiUnder conditions of being in context c, in time period ti+1By in the general of context c '
Rate；It is user in time period ti-1Under conditions of being in context c ', in time period tiProbability in context c；
It is user in time period ti-1Under conditions of being in context c ", in time period tiProbability in context c；Exist for user
Time period tiUnder conditions of being in context c, in time period ti+1Context c " probability will be in；
It is user in time period ti-1Under conditions of being in context c ', in time period tiNormalizing in context c
Change probability；Calculated by following formula：
Preferably, perception data is submitted to comprise the following steps according to the probability calculated in the step S400：
S401：A number in random generation interval [0,1] is used as submission threshold value；
S402：If the submission threshold value is less than or equal to the submission probability, current perception data is submitted；Otherwise, submit
The perception data of non-present.
According to described method, a kind of sensitive data protection system is realized, the system includes statistical module, sets mould
Block, computing module and submission module；Wherein：
The statistical module, is used for：Count the tense related information of perception data correspondence context；
The setup module, is used for：Context sensitivity set, the risk threshold value of context sensitivity leakage are set；
The computing module, is used for：Set in the tense related information and setup module that are obtained based on statistical module
Context sensitivity set, the risk threshold value of context sensitivity leakage, calculate the submission probability of current perception data；
The submission module, is used for：The submission probability obtained based on computing module, with reference to the submission threshold value generated at random,
Selection perception data is submitted.
The advantage of the disclosure compared with prior art is：
1) present disclose provides a kind of fine-grained protecting sensitive data method, based on current perception data to previous moment
The disclosure risk that the sensitive data perceived with later moment in time is triggered, determines to submit current perception data still to submit by calculating
Non-present perception data, so as to ensure the disclosure risk of user's sensitive data in controlled range.
2) local tense of the method for disclosure when submit perception data decision-making only only in accordance with current perception data is closed
Join information, while ensureing that the disclosure risk of user's sensitive data is in controlled range, required computing resource and storage is provided
Source is smaller, is more suitable for realizing on Intelligent mobile equipment.
Brief description of the drawings
Fig. 1 is the protecting sensitive data scene graph of disclosure Intelligent mobile equipment；
Fig. 2 is the flow chart for the protecting sensitive data method that the disclosure is proposed；
Fig. 3 is context schematic diagram residing for time adjacent segments user in disclosure one embodiment；
Fig. 4 is context schematic diagram residing for time adjacent segments user in disclosure one embodiment.
Embodiment
Fig. 1 is the protecting sensitive data scene graph of disclosure Intelligent mobile equipment, in the scene graph, the hand-held intelligence of user
Energy mobile device is by the sensor senses data such as GPS, WiFi, Mic, the protecting sensitive data method processing by the disclosure
Afterwards, context-aware applications are submitted to；The perception data that context-aware applications are submitted based on user, so as to provide the user
Corresponding personalized, context-sensitive service, meanwhile, context-aware applications may be information leakages such as perception datas
To attacker.The protecting sensitive data method of the disclosure is used as a bridge between Intelligent mobile equipment and context-aware applications
Beam, serves the purpose of protection user's sensitive data.
In one embodiment it is proposed that a kind of protecting sensitive data method, methods described includes as shown in Figure 2 following
Step：
S100, statistics perception data correspondence context tense related information；
S200, the risk threshold value for setting context sensitivity set, context sensitivity to reveal；
S300, based on the tense related information, context sensitivity set, risk threshold value, calculate current perception data
Submit probability；
Threshold value is submitted in S400, random generation, and the submission probability obtained according to calculating, selection perception data is submitted.
Further, the tense related information of perception data is counted in the step S100, is comprised the steps：
S101, one period set T={ t of acquisition1, t2... } represent, wherein, tiFor the i-th period, i=1,
2 ..., N, N are the sum of period；
S102, the various contexts being likely to occur to each period, deduction on the period, with set CiDuring expression
Between section tiOn the various contexts that are likely to occur；And the probability that each context occurs on each period is counted, useMark
Know in time period tiOn go out occurrence context c probability, wherein c ∈ Ci, i=1,2 ..., N；
S103, for two adjacent periods, count from a context transfer of previous time period to latter time
The probability of another context of section；WithRepresent in time period tiUnder conditions of place context c, in next time period ti+1Will
Probability in context c ', wherein c, c ' ∈ Ci, i=1,2..., N-1.
Optionally, the daily time of a user is divided into several consecutive hourss by a kind of step S101 be achieved in that
Between section, such as each hour is divided into a period, is divided into 24 periods, respectively 1,2,3 ..., 24, and
Be 1 (the 1st period of the 2nd day) in 24 next period, be next 2 successively, 3 ..., 24, so constantly circulate
Back and forth.Therefore, if ti=6, then its next time period ti+1=7, its a upper time period ti-1=5；If ti=24, then
Its next time period ti+1=1, its a upper time period ti-1=23.
Optionally, user can be divided into week and week the time weekly by step S101 another be achieved in that
End, then respectively in week and time that weekend is daily carries out discretization.
The risk threshold value δ that context sensitivity is revealed in the step S200 is the number between (0,1), and its numerical value is bigger,
Represent that the risk that user allows its context sensitivity to reveal is bigger, so that attacker conjecture user is in correct context sensitivity
Probability it is bigger, conversely, its numerical value is smaller, the possibility that context sensitivity is guessed right by attacker is smaller.
It is pointed out that once user specifies the risk threshold value δ of its context sensitivity leakage, then method of disclosure
It will ensure that any attacker guesses that user is in the probability of context sensitivity in controlled range, i.e., at attacker's conjecture user
Such as lower inequality is met in the probability of context sensitivity：
Wherein,Represent that attacker guesses user in time period tiContext sensitivity c probability is in,Represent that user exists
Time period tiIt is in context sensitivity c probability, wherein c ∈ Ci。
It is further noted that no matter user is in time period tiWhether context sensitivity c is in, if attacker guesses
Survey it and be in context sensitivity c, then attacker guesses that correct probability is at leastTherefore, if attacker is quick to user
Feel context and guess that correct probability is bigger, then the compromised risk of user's sensitive data is bigger.The purpose of attacker is exactly
So thatAnd the purpose of the present invention is to ensure that attacker conjecture user is correct in context sensitivity
Rate meets formulaUnder conditions of so that user is supplied to the perception data of context-aware applications to reach most
Bigization.
Preferably, probability is submitted to be calculated by following formula in the step S300：
And following condition is set up,
In formula：
CiIt is user in time period tiThe set of context being likely to be at；S is the context sensitivity set that user is set, i=
2,3 ..., N-1；
pC, cIt is the probability that the corresponding perception datas of context c are submitted when user is in context c；pC ', cIt is to work as user
The probability of the data corresponding to context c is submitted when being in context c ', wherein, c ' ≠ c；
δ is the risk threshold value for the context sensitivity leakage that user is set；
It is user in time period tiIt is in context c probability；It is user in time period ti-1It is in context c '
Probability,It is user in time period ti+1It is in context c " probability；It is user in time period tiIt is in context c '
Probability；
It is user in time period tiUnder conditions of being in context c, in time period ti+1By in the general of context c '
Rate；It is user in time period ti-1Under conditions of being in context c ', in time period tiProbability in context c；
It is user in time period ti-1Under conditions of being in context c ", in time period tiProbability in context c；Exist for user
Time period tiUnder conditions of being in context c, in time period ti+1Context c " probability will be in；
It is user in time period ti-1Under conditions of being in context c ', in time period tiNormalizing in context c
Change probability；Calculated by following formula：
Preferably, perception data is submitted to comprise the following steps according to the probability calculated in the step S400：
S401：A number in random generation interval [0,1] is used as submission threshold value；
S402：If the submission threshold value is less than or equal to the submission probability, current perception data is submitted；Otherwise, submit
The perception data of non-present.
The perception data of non-present described here, the perception data of the non-present refers to differ with current perception data
, the perception data forged, and the data of the forgery are still meaningful, can derive a context, and user
Above-mentioned derived context is likely to be in the time period t, so that attacker is difficult to guess that the perception data is
It is current or non-present, and then the difficulty that attacker conjecture user is presently in context is added, simultaneously as adjacent
There is tense incidence relation between period context, therefore, the context residing for active user if attacker has guessed wrong, that
The possibility for continuing to guess wrong in subsequent period of time can be greatly increased.
Example below combination accompanying drawing 3 illustrates the application of method of disclosure.
In one embodiment, period set T={ t1, t2, t3, the period is presently in for t2.Assuming that according to history
Data, are inferred to user in time period t2Upper residing set of context is { c2, c3, in time period t1And t3Upper user can be at
Set of context is respectively { c1And { c4}。
User is counted in time period t1In context c1ProbabilityFor 1.0, in time period t2It is upper to be in context c2
ProbabilityFor 0.5, in time period t2It is upper to be in context c3ProbabilityFor 0.5, and in time period t3On be in above and below
Literary c4ProbabilityFor 1.0.
User is counted in time period t1Residing context c1Under conditions of, in time period t2It is upper to be in context c2It is general
RateFor 0.5；User is in time period t1Residing context c1Under conditions of, in time period t2It is upper to be in context c3ProbabilityFor 0.5；User is in time period t2Residing context c2Under conditions of, in time period t3It is upper to be in context c4Probability
For 1.0；User is in time period t2Residing context c3Under conditions of, in time period t3It is upper to be in context c4ProbabilityFor
1.0.In figure 3, represent that the numeral near the context that user is likely to occur, circle represents that user goes out in the context with circle
Existing probability, is represented from the context transfer where directed edge starting point to directed edge with the numeral on the directed edge between context
The probability of the context of sensing, such as in time period t1And t2Between from context c1In the presence of sensing context c2Directed edge, thereon
Numeral 0.5 beValue.
The context sensitivity collection that user is set is combined into { c3, risk threshold value δ=0.05 of context sensitivity leakage, if currently
Residing context is c3.The submission probability of current perception data is calculated according to formula (1)：
Following condition is set up：
Above-mentioned equation is solved, can be obtainedWherein,It is general to submit
Rate.The number generated at random in interval [0,1] is 0.5, as threshold value is submitted, due to 0.5 less than or equal to submission generally
RateTherefore user submits current perception data.
In another embodiment, the perception data of statistics is constant, the tense related information of its corresponding context and upper
One embodiment is identical, and current slot is t2, the context being presently in is c3, except that being given birth at random in interval [0,1]
Into a number be 0.6, as submit threshold value.Because 0.6 more than submission probabilityThen user submits one
The perception data of non-present, and the context that the perception data is derived belongs to context c of the user in current slot2。
In another embodiment, compared with a upper embodiment, the perception data of statistics is constant, its corresponding context
Tense related information is identical with a upper embodiment, and current slot is still t2, but infer that its is corresponding according to current perception data
Context is c2If the submission threshold value generated at random is 0.5, because it is less than or equal to submit probabilityTherefore
User submits current perception data.
In another embodiment, compared with a upper embodiment, the perception data of statistics is constant, its corresponding context
Tense related information is identical with a upper embodiment, and current slot is still t2, according to current perception data infer its it is corresponding on
Hereinafter c2If the submission threshold value generated at random is 0.7, probability is submitted because it is more thanTherefore user submits
The perception data of one non-present, the context that the perception data is derived belongs to context of the user in current slot
c3。
In this way, attacker sees the perception data currently submitted, and derives corresponding to the perception data
Context is context sensitivity c3, but be difficult to distinguish whether the perception data is the currently practical perception data of user, so as to increase
Current attack person has been added to guess the difficulty of actual, real context residing for user.
Example below combination Fig. 4 illustrates the application of method of disclosure.
In one embodiment, period set T={ t3, t4, t5, current slot is t4.Assuming that according to time period t4
History perception data before, is inferred to user in time period t4Upper user can be at set of context { c3, c4, in the period
t3And t5It is respectively { c that upper user, which can be at set of context,1, c2And { c5, c6}；Meanwhile, user can be counted in each period
tiThe upper probability in each possible context；Circle in Fig. 4 is represented near the context that user is likely to occur, circle
Numeral represents the probability that user occurs in the context, such as in time period t3When the user that counts be in context c1Probability
ForIn time period t4When, user is in context c3And c4Probability, be respectivelyWith
Numeral on directed edge in Fig. 4 between context represents what is pointed to from the context transfer where directed edge starting point to directed edge
The probability of context, the tense incidence relation between time adjacent segments context is reacted with this.Such as in time period t3And t4Between from
Context c1In the presence of sensing context c3Directed edge, numeral 0.5 thereon isValue.
In the present embodiment, user sets context sensitivity collection to be combined into { c2, c4, c5, while setting context sensitivity to reveal
Risk threshold value δ=0.1.Assuming that the context according to residing for current perception data infers user is c4, calculate current according to formula (1)
The submission probability of perception data under context,
Following condition is set up：
In formula：
As seen in Figure 4,For 0.5,For 0.393,For 0.5,For 0.45.Calculated according to formula (2)：
User is in time period t3It is in context c2Under conditions of, in time period t4In context c3Normalization probability：
And user is in time period t3It is in context c2Under conditions of, in time period t4In context c4Normalization
Probability：
Above-mentioned equation is solved, can be obtainedWithWherein：It is when above and below user is in
Literary c3When submit context c3Corresponding perception data probability,It is when user is in context c4When submit context c4
The probability of corresponding perception data.
The number of random generation one in interval [0,1] is 0.4, because it is less than or equal to submit probability
Therefore user submits current perception data.
In another embodiment, if the number of random generation one in interval [0,1] is 0.5, submitted because it is more than
ProbabilityThen user submits the perception data of a non-present, and the context that the perception data is derived belongs to
Context c of the user in current slot3。
In another embodiment shown in Fig. 4, if the context according to residing for current perception data infers user is c3, then
Submit probability beRandom number in interval [0,1] is 0.4, because it is less than or equal to 0.583, therefore user
Submit current perception data.
According to described method, a kind of sensitive data protection system is realized, the system includes statistical module, sets mould
Block, computing module and submission module；Wherein：
The statistical module, is used for：Count the tense related information of perception data correspondence context；
The setup module, is used for：Context sensitivity set, the risk threshold value of context sensitivity leakage are set；
The computing module, is used for：Set in the tense related information and setup module that are obtained based on statistical module
Context sensitivity set, the risk threshold value of context sensitivity leakage, calculate the submission probability of current perception data；
The submission module, is used for：The submission probability obtained based on computing module, with reference to the submission threshold value generated at random,
Selection perception data is submitted.
The disclosure is described in detail above, used herein specific case principle of this disclosure and embodiment party
Formula is set forth, and the explanation of above example is only intended to help and understands disclosed method and its core concept；Meanwhile, it is right
In those skilled in the art, according to the thought of the disclosure, it will change in specific embodiments and applications, it is comprehensive
Upper described, this specification content should not be construed as limitation of this disclosure.
Claims (2)
1. a kind of protecting sensitive data method, it is characterised in that methods described comprises the steps：
S100, statistics perception data correspondence context tense related information；
S200, the risk threshold value for setting context sensitivity set, context sensitivity to reveal；
S300, based on the tense related information, context sensitivity set, risk threshold value, calculate the submission of current perception data
Probability；
Threshold value is submitted in S400, random generation, and the submission probability obtained according to calculating, selection perception data is submitted；Specific bag
Include following step：
S401：A number in random generation interval [0,1] is used as submission threshold value；
S402：If the submission threshold value is less than or equal to the submission probability, current perception data is submitted；Otherwise, non-work as is submitted
Preceding perception data；
Probability is submitted to be calculated by following formula in the step S300：
And following condition is set up：
1)If c is not context sensitivity,
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<mi>P</mi>
<msup>
<mi>c</mi>
<mo>&prime;</mo>
</msup>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</msubsup>
<msubsup>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mrow>
<msup>
<mi>c</mi>
<mo>&prime;</mo>
</msup>
<mo>,</mo>
<mi>c</mi>
</mrow>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</msubsup>
</mfrac>
<mo>,</mo>
<mfrac>
<msubsup>
<mi>P</mi>
<msup>
<mi>c</mi>
<mrow>
<mo>&prime;</mo>
<mo>&prime;</mo>
</mrow>
</msup>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
</msubsup>
<msubsup>
<mi>P</mi>
<mrow>
<mi>c</mi>
<mo>,</mo>
<msup>
<mi>c</mi>
<mrow>
<mo>&prime;</mo>
<mo>&prime;</mo>
</mrow>
</msup>
</mrow>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
</msubsup>
</mfrac>
<mo>,</mo>
<msubsup>
<mi>P</mi>
<mi>c</mi>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
</msubsup>
<mo>+</mo>
<mi>&delta;</mi>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
3)If c is context sensitivity,
<mrow>
<msubsup>
<mi>P</mi>
<mi>c</mi>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
</msubsup>
<mo>&CenterDot;</mo>
<mrow>
<mo>(</mo>
<mn>1.0</mn>
<mo>-</mo>
<msub>
<mi>p</mi>
<mrow>
<mi>c</mi>
<mo>,</mo>
<mi>c</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<munder>
<mo>&Sigma;</mo>
<mrow>
<msup>
<mi>c</mi>
<mo>&prime;</mo>
</msup>
<mo>&NotEqual;</mo>
<mi>c</mi>
</mrow>
</munder>
<msub>
<mi>p</mi>
<mrow>
<msup>
<mi>c</mi>
<mo>&prime;</mo>
</msup>
<mo>,</mo>
<mi>c</mi>
</mrow>
</msub>
<mo>&CenterDot;</mo>
<msubsup>
<mi>P</mi>
<msup>
<mi>c</mi>
<mo>&prime;</mo>
</msup>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
</msubsup>
<mo>;</mo>
</mrow>
In formula：
CiIt is user in time period tiThe set of context being likely to be at；
S is the context sensitivity set that user is set, i=2,3 ..., N-1；N is the sum of period；
pc,cIt is the probability that the perception data corresponding to context c is submitted when user is in context c；
pc',cIt is the probability that the perception data corresponding to context c is submitted when user is in context c', wherein, c' ≠ c；
δ is the risk threshold value for the context sensitivity leakage that user is set；
It is user in time period tiIt is in context c probability；It is user in time period ti-1It is in the general of context c'
Rate,It is user in time period ti+1It is in context c " probability；It is user in time period tiIt is in the general of context c'
Rate；
It is user in time period tiUnder conditions of being in context c, in time period ti+1Context c " probability will be in；
It is user in time period ti-1Under conditions of being in context c', in time period tiNormalization in context c is general
Rate；Calculated by following formula：
<mrow>
<msubsup>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mrow>
<msup>
<mi>c</mi>
<mo>&prime;</mo>
</msup>
<mo>,</mo>
<mi>c</mi>
</mrow>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</msubsup>
<mo>=</mo>
<mfrac>
<msubsup>
<mi>P</mi>
<mrow>
<msup>
<mi>c</mi>
<mo>&prime;</mo>
</msup>
<mo>,</mo>
<mi>c</mi>
</mrow>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</msubsup>
<mrow>
<munder>
<mo>&Sigma;</mo>
<mrow>
<msup>
<mi>c</mi>
<mrow>
<mo>&prime;</mo>
<mo>&prime;</mo>
</mrow>
</msup>
<mo>&Element;</mo>
<msub>
<mi>C</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</mrow>
</munder>
<msubsup>
<mi>P</mi>
<mrow>
<msup>
<mi>c</mi>
<mrow>
<mo>&prime;</mo>
<mo>&prime;</mo>
</mrow>
</msup>
<mo>,</mo>
<mi>c</mi>
</mrow>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</msubsup>
</mrow>
</mfrac>
<mo>,</mo>
</mrow>
In formula：
It is user in time period ti-1Under conditions of being in context c', in time period tiProbability in context c；
It is user in time period ti-1Under conditions of being in context c ", in time period tiProbability in context c；
Ci-1Represent time period ti-1On the various set of context that are likely to occur.
2. a kind of sensitive data protection system that method according to power 1 is realized, the system includes statistical module, sets mould
Block, computing module and submission module；Wherein：
The statistical module, is used for：Count the tense related information of perception data correspondence context；
The setup module, is used for：Context sensitivity set, the risk threshold value of context sensitivity leakage are set；
The computing module, is used for：The sensitivity set in the tense related information and setup module that are obtained based on statistical module
Set of context, the risk threshold value of context sensitivity leakage, calculate the submission probability of current perception data；
The submission module, is used for：The submission probability obtained based on computing module, with reference to the submission threshold value generated at random, selection
Perception data is submitted；
Wherein, probability is submitted to be calculated by following formula described in the computing module：
And following condition is set up：
1)If c is not context sensitivity,
<mrow>
<msub>
<mi>p</mi>
<mrow>
<mi>c</mi>
<mo>,</mo>
<mi>c</mi>
</mrow>
</msub>
<mo>&le;</mo>
<munder>
<mi>min</mi>
<mrow>
<msup>
<mi>c</mi>
<mo>&prime;</mo>
</msup>
<mo>,</mo>
<msup>
<mi>c</mi>
<mrow>
<mo>&prime;</mo>
<mo>&prime;</mo>
</mrow>
</msup>
<mo>&Element;</mo>
<mi>S</mi>
</mrow>
</munder>
<mrow>
<mo>(</mo>
<mfrac>
<msubsup>
<mi>P</mi>
<msup>
<mi>c</mi>
<mo>&prime;</mo>
</msup>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</msubsup>
<msubsup>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mrow>
<msup>
<mi>c</mi>
<mo>&prime;</mo>
</msup>
<mo>,</mo>
<mi>c</mi>
</mrow>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</msubsup>
</mfrac>
<mo>,</mo>
<mfrac>
<msubsup>
<mi>P</mi>
<msup>
<mi>c</mi>
<mrow>
<mo>&prime;</mo>
<mo>&prime;</mo>
</mrow>
</msup>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
</msubsup>
<msubsup>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mrow>
<mi>c</mi>
<mo>,</mo>
<msup>
<mi>c</mi>
<mrow>
<mo>&prime;</mo>
<mo>&prime;</mo>
</mrow>
</msup>
</mrow>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
</msubsup>
</mfrac>
<mo>,</mo>
<mn>1.0</mn>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
2)If c is context sensitivity,
<mrow>
<msub>
<mi>p</mi>
<mrow>
<mi>c</mi>
<mo>,</mo>
<mi>c</mi>
</mrow>
</msub>
<mo>&le;</mo>
<munder>
<mi>min</mi>
<mrow>
<msup>
<mi>c</mi>
<mo>&prime;</mo>
</msup>
<mo>,</mo>
<msup>
<mi>c</mi>
<mrow>
<mo>&prime;</mo>
<mo>&prime;</mo>
</mrow>
</msup>
<mo>&Element;</mo>
<mi>S</mi>
</mrow>
</munder>
<mrow>
<mo>(</mo>
<mfrac>
<msubsup>
<mi>P</mi>
<msup>
<mi>c</mi>
<mo>&prime;</mo>
</msup>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</msubsup>
<msubsup>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mrow>
<msup>
<mi>c</mi>
<mo>&prime;</mo>
</msup>
<mo>,</mo>
<mi>c</mi>
</mrow>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</msubsup>
</mfrac>
<mo>,</mo>
<mfrac>
<msubsup>
<mi>P</mi>
<msup>
<mi>c</mi>
<mrow>
<mo>&prime;</mo>
<mo>&prime;</mo>
</mrow>
</msup>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
</msubsup>
<msubsup>
<mi>P</mi>
<mrow>
<mi>c</mi>
<mo>,</mo>
<msup>
<mi>c</mi>
<mrow>
<mo>&prime;</mo>
<mo>&prime;</mo>
</mrow>
</msup>
</mrow>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
</msubsup>
</mfrac>
<mo>,</mo>
<msubsup>
<mi>P</mi>
<mi>c</mi>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
</msubsup>
<mo>+</mo>
<mi>&delta;</mi>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
3)If c is context sensitivity,
<mrow>
<msubsup>
<mi>P</mi>
<mi>c</mi>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
</msubsup>
<mo>&CenterDot;</mo>
<mrow>
<mo>(</mo>
<mn>1.0</mn>
<mo>-</mo>
<msub>
<mi>p</mi>
<mrow>
<mi>c</mi>
<mo>,</mo>
<mi>c</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<munder>
<mo>&Sigma;</mo>
<mrow>
<msup>
<mi>c</mi>
<mo>&prime;</mo>
</msup>
<mo>&NotEqual;</mo>
<mi>c</mi>
</mrow>
</munder>
<msub>
<mi>p</mi>
<mrow>
<msup>
<mi>c</mi>
<mo>&prime;</mo>
</msup>
<mo>,</mo>
<mi>c</mi>
</mrow>
</msub>
<mo>&CenterDot;</mo>
<msubsup>
<mi>P</mi>
<msup>
<mi>c</mi>
<mo>&prime;</mo>
</msup>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
</msubsup>
<mo>;</mo>
</mrow>
In formula：
CiIt is user in time period tiThe set of context being likely to be at；
S is the context sensitivity set that user is set, i=2,3 ..., N-1；N is the sum of period；
pc,cIt is the probability that the perception data corresponding to context c is submitted when user is in context c；
pc',cIt is the probability that the perception data corresponding to context c is submitted when user is in context c', wherein, c' ≠ c；
δ is the risk threshold value for the context sensitivity leakage that user is set；
It is user in time period tiIt is in context c probability；It is user in time period ti-1It is in the general of context c'
Rate,It is user in time period ti+1It is in context c " probability；For user at time period t i in the general of context c'
Rate；
It is user in time period tiUnder conditions of being in context c, in time period ti+1Context c " probability will be in；
It is user in time period ti-1Under conditions of being in context c', in time period tiNormalization in context c is general
Rate；Calculated by following formula：
<mrow>
<msubsup>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mrow>
<msup>
<mi>c</mi>
<mo>&prime;</mo>
</msup>
<mo>,</mo>
<mi>c</mi>
</mrow>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</msubsup>
<mo>=</mo>
<mfrac>
<msubsup>
<mi>P</mi>
<mrow>
<msup>
<mi>c</mi>
<mo>&prime;</mo>
</msup>
<mo>,</mo>
<mi>c</mi>
</mrow>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</msubsup>
<mrow>
<munder>
<mo>&Sigma;</mo>
<mrow>
<msup>
<mi>c</mi>
<mrow>
<mo>&prime;</mo>
<mo>&prime;</mo>
</mrow>
</msup>
<mo>&Element;</mo>
<msub>
<mi>C</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</mrow>
</munder>
<msubsup>
<mi>P</mi>
<mrow>
<msup>
<mi>c</mi>
<mrow>
<mo>&prime;</mo>
<mo>&prime;</mo>
</mrow>
</msup>
<mo>,</mo>
<mi>c</mi>
</mrow>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</msubsup>
</mrow>
</mfrac>
<mo>,</mo>
</mrow>
In formula：
It is user in time period ti-1Under conditions of being in context c', in time period tiProbability in context c；
It is user in time period ti-1Under conditions of being in context c ", in time period tiProbability in context c；
Ci-1Represent time period ti-1On the various set of context that are likely to occur.
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