A dual-objective optimization task allocation method for privacy protection in mobile crowd sensing

Abstract

The application discloses a double-target optimization task allocation method for privacy protection in mobile crowd sensing, and the method is that a task requester creates a sensing task containing a task position and a task budget, and sends the sensing task to a sensing platform SP, then a server calculates a task utility of each task participant according to the sensing task and a position of the task participant, and selects a suitable task participant to participate in the task in combination with the task budget, so that social welfare and income of the task requester are maximized, and finally, the task participant of the sensing task moves to the task position to execute the sensing task. The application proposes a secure Manhattan distance calculation protocol SMD and a secure division protocol SDIV based on a Paillier TD cryptosystem, and the privacy data of the task participant and the task requester are protected, and bilateral privacy protection is realized. A privacy protection dynamic programming algorithm is designed based on the two protocols, and the target of maximizing social welfare and task requester benefit is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of information security, in particular to a dual-target optimization task allocation method for privacy protection in mobile crowd sensing. BACKGROUND

[0002] The emergence of mobile crowd sensing is a new sensing paradigm, which has been widely applied and researched. Mobile crowd sensing collects sensing data by outsourcing sensing tasks to task participants with the assistance of a sensing platform. Mobile crowd sensing provides a flexible and inexpensive way of collecting sensing data. Accordingly, mobile crowd sensing is widely used in environmental monitoring, social services, traffic prediction and behavior sensing, etc. Task allocation is a key technology for allocating sensing tasks to suitable participants. The task allocation mechanism generally includes a task requester, a task participant and a sensing platform. The sensing platform selects suitable and competent task participants according to the task constraints of the task requester, and the selection operation is usually modeled as an optimization problem. Among them, the task constraints are crucial to the optimization problem of task allocation. Time and space are the most common constraints in task allocation.

[0003] However, these restrictions are likely to leak the privacy of participants and requesters, such as location privacy and task privacy. In the case of privacy protection, the more constraints and optimization targets, the more difficult it is to output the optimal solution. The current target optimization task allocation for privacy protection usually uses perturbation, confusion or encryption to protect location information. However, the method based on perturbation or confusion makes it difficult to calculate the distance between the task participant and the sensing task. In addition, encrypted locations may increase the high computational and time costs, which may lead to untimely response to task allocation. In addition, there are many optimization algorithms used in task allocation, however, greedy algorithm and genetic algorithm often produce uncertain results, rather than determining an optimal solution.

[0004] Therefore, in view of the problems existing in the prior art, there is an urgent need for an efficient and accurate dual-target optimization task allocation method for privacy protection. SUMMARY

[0005] The present application aims to overcome the shortcomings of the prior art and provides a dual-target optimization task allocation method for privacy protection in mobile crowd sensing, which can effectively solve the location privacy problem of participants and task requesters, and at the same time achieve the goal of maximizing social welfare and requester income.

[0006] To achieve the above objectives, the technical solution provided by this invention is as follows: a dual-objective optimization task allocation method for privacy protection in mobile group perception, wherein the mobile group perception includes a task requester, a perception platform SP, a cloud computing service provider CSP, and n task participants; the method involves the task requester creating a perception task containing task location and task budget, and sending it to the perception platform SP; then the server calculates the task utility of each task participant based on the perception task and the location of the task participants, and selects suitable task participants to participate in the task in combination with the task budget, thereby maximizing social welfare and the income of the task requester; finally, the task participants who obtained the perception task move to the task location to execute the perception task; wherein, in order to protect the location privacy of the perception task and the task participants, the task requester and the task participants use a threshold-decrypted Paillier cryptosystem to protect their respective location information and send it to the perception platform SP; to protect the location information of the task participants and the task requester from being leaked, the perception platform SP and the cloud computing service provider CSP jointly execute a secure computing protocol to reasonably allocate the task budget to the task participants, so as to achieve the dual objectives of maximizing social welfare and the income of the task requester.

[0007] Furthermore, the dual-objective optimization task allocation method for privacy protection in mobile group perception includes the following steps:

[0008] S1. The task requester initializes the PaillierTD cryptosystem and publishes the public key of the PaillierTD cryptosystem. and the private key Divided into two parts, namely a partial private key and and will and Send them separately to the perception platform SP and the cloud computing service provider CSP;

[0009] S2, The task requester issues a perception task. ,in Indicates the mission location, which includes the longitude of the mission location. and the latitude of the mission location , These represent the start and end times of the task, respectively. To represent the task budget, the task requester uses a Paillier cryptosystem with threshold decryption to encrypt the task location, start time, and end time, respectively, resulting in encrypted task location, start time, and end time. ,in This indicates an encryption operation, followed by the encryption perception task. Send to the perception platform SP;

[0010] S3, the task participant protects the participant's information by using a Paillier cryptosystem with threshold decryption wherein represents a task participant , represents a task participant 's perceived speed, obtaining encrypted task participant information , and then sending to the sensing platform SP;

[0011] S4, in order to achieve the two goals of maximizing social welfare and requester benefit, the sensing platform SP jointly performs privacy protection task allocation with the task participant, and then allocates the sensing task to a suitable task participant;

[0012] S5, the task participant moves to the task location to perform the sensing task and obtains the corresponding remuneration, while generating a benefit for the task requester.

[0013] Further, in step S1, the task requester initializes the paillierTD cryptosystem, selects two large prime numbers , and calculates the integer and the generator , and calculates the private key , wherein lcm() represents the least common multiple function, and the public key of the paillierTD cryptosystem is disclosed , the task requester divides the private key into and , , , is a random number.

[0014] Further, in step S2 and step S3, the task location and the task participant location are represented as , , respectively, wherein should be converted into an integer in advance, represents the longitude of the task participant location, represents the latitude of the task participant location, and the Manhattan distance from the task participant location to the task location is: .

[0015] Further, in step S4, a task requester and task participant, the goal of task allocation is to maximize social welfare and requester benefit, the sensing platform SP and the computing service provider CSP calculate the Manhattan distance from the current location of each task participant to the location of the task based on the secure Manhattan distance calculation protocol SMD, in the SMD, the sensing platform SP inputs 、 、 、 wherein ; in addition, the sensing platform SP has a public key and a partial private key generated by the Paillier TD cryptosystem , the cloud computing service provider CSP has a public key and a partial private key generated by the Paillier TD cryptosystem , the specific steps of the SMD are as follows:

[0016] S411, the sensing platform SP selects a random number , the random number , so that , wherein is a security parameter; the sensing platform SP generates a random coin flip , that is ;

[0017] S412, if , then the public key is used to encrypt to obtain , and the partial decryption can be obtained; if , then the public key is used to encrypt to obtain , and the partial decryption can be obtained;

[0018] S413, the sensing platform SP uses the partial private key and the partial decryption to obtain the decryption , and calculates , and finally sends these data to the cloud computing service provider CSP;

[0019] S414, after the cloud computing service provider CSP receives the data, uses the partial private key to partially decrypt, obtains the decryption , and based on decrypts to obtain the intermediate parameter ;

[0020] S415、if , then intermediate parameter ; otherwise, ; cloud computing service provider CSP computes intermediate parameter , i.e. , and sends it to sensing platform SP.

[0021] S416, sensing platform SP computes the absolute value of the two numbers under the ciphertext, and gets .

[0022] S417, sensing platform SP and cloud computing service provider CSP repeat the above steps S411-S416 to compute , and finally multiply and to get .

[0023] Further, in step S4, sensing platform SP, cloud computing service provider CSP and task participant use secure division protocol SDIV to obtain the time consumed by the task participant to reach the task location, and the specific steps of SDIV are as follows:

[0024] S421, sensing platform SP randomly selects bit parameter , and computes intermediate parameter based on the addition and scalar multiplication under the ciphertext, i.e. .

[0025] S422, sensing platform SP uses partial private key and intermediate parameter to get decryption , and sends to CSP.

[0026] S423, cloud computing service provider CSP uses partial private key and intermediate parameter after the Paillier TD cryptosystem, gets decryption , and based on completely decrypts to get intermediate parameter , and finally sends it to task participant .

[0027] S424, task participant has the sensing speed of the task , computes intermediate parameter , and then calls the encryption function based on the Paillier TD cryptosystem to get and sends it to the sensing platform SP;

[0028] S425, finally, the sensing platform SP calculates the required time for the task participant to get from the current location to the task location .

[0029] Further, in step S4, the sensing platform SP calculates the reward of the task participant and the value provided to the society , the specific steps are as follows:

[0030] S431, the sensing platform SP calculates the time required for collecting the sensing data ;

[0031] S432, based on the Paillier TD cryptosystem, the sensing platform SP uses the partial private key and partial decryption , and sends it to the CSP ;

[0032] S433, the cloud computing service provider CSP uses the partial private key and decryption , gets the partial decryption , and based on , full decryption the time required for the task participant to collect the sensing data , and sends it to the sensing platform SP ;

[0033] S434, assuming that the efficiency of the task participant collecting the sensing data is fixed, then the utility of the task participant is proportional to the time for collecting the sensing data, the specific formula is: ;

[0034] S435, the sensing platform SP calculates the reward of the task participant for collecting the sensing data and the social value provided by it according to the utility of each task participant , the specific formula is: , , where is a hyperparameter used to describe the relationship between utility and reward / value.

[0035] Further, in step S4, the sensing platform SP realizes the two goals of maximizing social welfare and requester revenue based on the dynamic programming algorithm, the specific steps are as follows:

[0036] S441, the sensing platform SP initializes a dynamic programming table , size is , where n is the number of task participants, is the task budget of the task requester;

[0037] S442, traverse all task participants i and the current task budget j, if the reward of the current task participant does not exceed the task budget, that is , it is necessary to calculate the maximum social value of selecting or not selecting the task participant i, that is ;

[0038] S443, if the reward of the current task participant exceeds the task budget, that is , only the task participant i can not be selected, that is ;

[0039] S444, the sensing platform SP creates a selected participant list, traverses all task participants from the last task participant, and judges whether it is equal to , if it is equal, it means that the task participant i is selected, then the task participant i is added to the selected task participant list, and then the remaining budget of the task is updated, that is, the task budget is reduced by the reward of the task participant i, until the task budget is 0.

[0040] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0041] 1. The present application can effectively solve the double-target optimization task allocation problem of privacy protection in mobile group sensing.

[0042] 2. The present application proposes a secure Manhattan distance calculation protocol SMD and a secure division protocol SDIV based on the Paillier TD cryptosystem, which protects the privacy data of the task participants and the task requester, and realizes bilateral privacy protection.

[0043] 3. Based on the above two protocols, a privacy protection dynamic programming algorithm is designed, which simultaneously realizes the goals of maximizing social welfare and task requester benefit, and accurately and efficiently allocates appropriate participants for sensing tasks. BRIEF DESCRIPTION OF DRAWINGS

[0044] Fig. 1 is the flowchart of the method of the present application.

[0045] Fig. 2 is the flowchart of the secure Manhattan distance calculation protocol provided by the present application.

[0046] Fig. 3 is the flowchart of the secure division protocol provided by the present application. DETAILED DESCRIPTION

[0047] The present invention will be further described below with reference to specific embodiments.

[0048] like Figs. 1 to 3 As shown, this embodiment provides a dual-objective optimization task allocation method for privacy protection in mobile group sensing. The mobile group sensing includes a task requester, a sensing platform SP, a cloud computing service provider CSP, and three task participants. The method involves the task requester creating a sensing task containing task location and task budget, and sending it to the sensing platform. The server then calculates the task utility for each participant based on the sensing task and the participant's location, and selects suitable participants based on the task budget, thereby maximizing social welfare and the task requester's income. Finally, the participants who have obtained the sensing task move to the task location to execute the sensing task. To protect the location privacy of the sensing task and participants, the task requester and participants use a threshold-decrypted Paillier cryptosystem to protect their respective location information before sending it to the sensing platform SP. To prevent the leakage of the location information of the participants and the requester, the sensing platform SP and the cloud computing service provider CSP jointly execute a secure computing protocol to reasonably allocate the task budget to the participants, achieving the dual objectives of maximizing social welfare and the task requester's income. Its specific implementation includes the following steps:

[0049] S1. The task requester initializes the PaillierTD cryptosystem and publishes the public key of the PaillierTD cryptosystem. Meanwhile, the PaillierTD cryptosystem uses part of the private key. and Distributed to perception platform SPs and cloud computing service providers CSPs;

[0050] S2, The task requester issues a perception task. ,in Indicates the mission location, which includes the longitude of the mission location. and the latitude of the mission location , These represent the start and end times of the task, respectively. To represent the task budget, the task requester uses a Paillier cryptosystem with threshold decryption to encrypt the task location, start time, and end time, obtaining... = (52448906577454270637114262046038532105046602818863094766875890882514210905569530767723622351614556256553860506000547166032349667986584058613299889883015,839786741518535489251552746097780886701399609483695052383343043811194630584545942267520678103605626006057179755104276952693529895781557534303802043784608), = 1173773108554760545164716182613851953612513021963454706295328237031116585096793648427709046034446814657974513979195201994674383248116617658416257284594318, then to the perception platform SP;

[0051] S3, the participants employ a Paillier cryptosystem with threshold decryption to protect the participants' information , obtain the encrypted participants' information , <(985200090305827610422738032090486005092876029919152255574961480541089594398692395289187512512417466860906243402635162981913377747206354242447199646176567, 909436023207867028941432644805344455857856894404878351865135586511480342511736249802594262672328494604482834002797977819439550743497817927565960217887469),1056772047371000064483024238428304557265810832127440372543404934331005179330528697633001611942740004261325823148922866544663467660379574046396930027559351> <(429733755620642735747997770539450373118245958599077389441463197672668362305904903978669163808458270094723464005374496365359126061067995653116595105112258, 1191323783335368143947601832738588345142595129371683539459378667397959635888910478188773736216986290786364485773342466290411488003103428044055318562639057),1118238832335109275879427071368843925033589538169494408693590152031212888287268151924885397903665396448998217953789003789697909743249582099530411705718310> < (622471470934968573014796454730341906422211902776626482364481609471771340623964092746772685018908538126409720357459415006556933063949887002295207262182207, 363255660417453190935816095559778522624334219262218749392938872469667917371916802310819139926148395445232493109760582342114160687244248844232403903488767), 404889571969303243447626331719392451803151639189523791486883445374338510536215899598785574692124325764654567440227294151773939593588576248232605893732484 >, then is sent to the perception platform SP;

[0052] S4, in order to maximize the two goals of social welfare and requester benefit, the perception platform SP jointly participates in the privacy protection task allocation, and then allocates the perception task to the appropriate participant, the present application includes a task requester and a participant, the goal of task allocation is to maximize the social welfare and requester benefit, and the specific steps of the double target task allocation of privacy protection are as follows:

[0053] S41, the perception platform SP and the cloud computing service provider CSP calculate the Manhattan distance from the current location of each participant to the location of the task based on the secure Manhattan distance calculation protocol SMD, in the SMD, the perception platform SP inputs , , , , wherein In addition, the perception platform SP has the public key and part of the private key generated by the Paillier TD cryptosystem The cloud computing service provider CSP has the public key and part of the private key generated by the Paillier TD cryptosystem , wherein The part of the private key , The specific steps of the SMD are as follows:

[0054] S411, the sensing platform SP selects a random number , the random number , such that , where is a security parameter, for example ; the sensing platform SP flips a random coin to generate , that is ;

[0055] S412, if , then the public key is used to encrypt to obtain , then the partial decryption can be obtained; if , then the public key is used to encrypt to obtain , then the partial decryption can be obtained

[0056] S413, the sensing platform SP uses the partial private key to partially decrypt to obtain the decrypted , and calculates , and finally sends these data to the cloud computing service provider CSP;

[0057] S414, after the cloud computing service provider CSP receives the data, uses the partial private key to partially decrypt, obtains the decrypted , and based on decryption obtains the intermediate parameter ;

[0058] S415, if , then the intermediate parameter ; otherwise, ; the cloud computing service provider CSP calculates , that is , and sends it to the sensing platform SP;

[0059] S416, the sensing platform SP calculates the absolute value of the two numbers under the ciphertext, and obtains ;

[0060] S417, the sensing platform SP and the cloud computing service provider CSP repeat the above steps S411-S416 to calculate , and finally send and multiplying, we get then 292001651135062154604085552875160104359921656604149559690336747998921305458302273552757092124410738297633303469777442034269228231089993690950858926908367, 1199128815711729445476568843975921411826722733690011126569389647607351735311259394537721546200871560854347151583101797882205907617515250237585209578104980, 765888551813321219605651161831090431205262258222092463296358333327625786536921430241151188134937499151706410339841371487037717991606668183209845585851040;

[0061] S42, the sensing platform SP, the cloud computing service provider CSP and the participant obtain the time consumed by the participant to reach the task location using the secure division protocol SDIV, and the specific steps of SDIV are as follows:

[0062] S421, the sensing platform SP randomly selects parameters of bits , calculates the intermediate parameters based on the addition and scalar multiplication under the ciphertext , that is ;

[0063] S422, the sensing platform SP obtains the decryption based on the Paillier TD cryptosystem using the partial private key and the intermediate parameters , and sends to the CSP;

[0064] S423, the cloud computing service provider CSP obtains the decryption based on the Paillier TD cryptosystem using the partial private key and the intermediate parameters , and based on Fully decrypting to obtain intermediate parameters , and finally sending them to the participant ;

[0065] S424, the participant has the perceived speed of the task , calculate the intermediate parameters , call the encryption function based on the Paillier TD cryptosystem, obtain , and send it to the sensing platform SP;

[0066] S425, finally, the sensing platform SP calculates , to obtain the required time for the participant to move from the current location to the task location 1913975206207092531200393280680952292736277157412908197110398616095787229724755728437606685522063682867054442108642894708964078481804895279569426659051047、 1462637051412083196670907139349732864273390948990546902839591508545675806141385325798036864471550797723109411569068717273620949772620521208016110906857250、 685949240286586643608389669522420550060072376398476243197500923192462420596610804215405466417774660009592814628024128069411863185674167385508276985059182。

[0067] S43, the sensing platform SP calculates the participant 's reward and the value provided to the society , the specific steps are as follows:

[0068] S431, the sensing platform SP calculates the time required to collect sensing data ;

[0069] S432, based on the Paillier TD cryptosystem, the sensing platform SP uses the partial private key and partial decryption , and sends to the CSP;

[0070] S433, the cloud computing service provider CSP uses the partial private key and decryption , get partial decryption , and based on , full decryption get the time needed for participants to collect sensing data , wherein 592, 382, 598 and send to the sensing platform SP;

[0071] S434, assuming the efficiency of the participants to collect sensing data is certain, then the utility of the participants is proportional to the time to collect sensing data, assuming full conversion between utility and time, then the efficiency , 592, 382, 598;

[0072] S435, the sensing platform SP calculates the reward of the participants to collect sensing data and the social value provided by each participant according to the utility of each participant , the specific formula is: , , wherein is a hyperparameter used to describe the relationship between utility and reward / value, assuming , then 592, 382, 598; 592, 382, 598.

[0073] S44, the sensing platform SP realizes the two goals of maximizing social welfare and requester revenue based on the dynamic programming algorithm, the specific steps are as follows:

[0074] S441, the sensing platform SP initializes a dynamic programming table , the size is , wherein n is the number of participants, and B is the task budget of the task requester;

[0075] S442, traverse all participants i and current task budget j, if the current participant's reward does not exceed the task budget, i.e. , it is necessary to calculate the maximum social value of selecting or not selecting the participant i, i.e. ;

[0076] S443, if the current participant's reward exceeds the task budget, i.e. , only participant i can be not selected, i.e. ;

[0077] S444, the sensing platform SP creates a selected participant list, traverses all participants from the last participant, and judges whether it is equal to , thus, the selected participant is only the third participant.

[0078] S5, the task participant moves to the task position to execute the sensing task and obtain the corresponding reward, and generates the income for the task requester.

[0079] The above embodiment is the preferred embodiment of the present application, but the embodiment of the present application is not limited by the above embodiment, and any change, modification, substitution, combination, simplification made without departing from the spirit and principle of the present application should be an equivalent replacement mode, which is included in the protection scope of the present application.

Claims

1. A method for double-objective optimization task allocation with privacy protection in mobile crowd sensing, the mobile crowd sensing comprising a task requester, a sensing platform (SP), a cloud computing service provider (CSP), and n task participants, characterized in that, The method is that a task requester creates a perception task containing a task location and a task budget, and sends it to a perception platform SP, then the server calculates the task utility of each task participant according to the perception task and the location of the task participant, and selects a suitable task participant to participate in the task in combination with the task budget, so as to maximize the social welfare and the income of the task requester, and finally the task participant of the perception task moves to the task location to execute the perception task; wherein, in order to protect the location privacy of the perception task and the task participant, the task requester and the task participant use a threshold decryption paillier encryption system to protect the location information of each other and send it to the perception platform SP; in order to protect the location information of the task participant and the task requester from being leaked, the perception platform SP and the cloud computing service provider CSP jointly execute a secure computation protocol to reasonably allocate the task budget to the task participant, so as to achieve the two goals of maximizing the social welfare and the income of the task requester; comprising the following steps: S1, the task requester initializes the paillier TD cryptosystem, discloses the public key of the paillier TD cryptosystem , and divides the private key into two parts, respectively, as partial private key and , and sends and to the perception platform SP and the cloud computing service provider CSP respectively; S2, a task requester publishes a sensing task wherein denotes a task location, which includes a longitude where the task location is located and a latitude where the task location is located , denotes a task start time and a task end time, respectively denotes a task budget, the task requester encrypts the task location, the task start time and the task end time by using a Paillier cryptosystem with threshold decryption to obtain encrypted task location, encrypted task start time and encrypted task end time, respectively wherein denotes an encryption operation, and then the encrypted sensing task is sent to a sensing platform SP; S3. The task participants use the Paillier cryptosystem with threshold decryption to protect their information. ,in Indicates task participants Location, Indicates task participants The perception speed is used to obtain encrypted task participant information. Then Send to the perception platform SP; S4, in order to achieve the two goals of maximizing the social welfare and the income of the requester, the perception platform SP jointly executes a privacy protection task allocation with the task participant, and then allocates the perception task to a suitable task participant; S5, the task participant moves to the task location to execute the perception task and obtains the corresponding remuneration, and at the same time generates income for the task requester. 2.The method of claim 1, wherein, In step S1, the task requester initializes the paillier TD cryptosystem, selects two large prime numbers , , and calculates the integer and the generator , and calculates the private key , wherein lcm() represents the least common multiple function, and the public key of the paillier TD cryptosystem is disclosed, the task requester divides the private key into and , , , is a random number. 3.The method of claim 2, wherein, In step S2 and step S3, the task position and the task participant position are respectively represented as , wherein should be converted into an integer in advance, represents the longitude where the task participant position is located, represents the latitude where the task participant position is located, and the Manhattan distance from the task participant position to the task position is: .

4. The method of claim 3, wherein, In step S4, including a task requester and a task participant, the goal of task allocation is to maximize social welfare and requester benefit, the sensing platform SP and the computing service provider CSP calculate the Manhattan distance from the current location of each task participant to the task location based on the secure Manhattan distance calculation protocol SMD, in which the sensing platform SP inputs 、 、 、 , wherein ; in addition, the sensing platform SP has the public key and part of the private key generated by the Paillier TD cryptosystem , the cloud computing service provider CSP has the public key and part of the private key generated by the Paillier TD cryptosystem , and the specific steps of SMD are as follows: S411, the sensing platform SP selects a random number , the random number , such that , wherein is a security parameter; the sensing platform SP tosses a random coin to generate , i.e. ; S412、if then encrypt using public key based on Paillier TD cryptosystem then partial decryption is possible ; if then encrypt using public key based on Paillier TD cryptosystem then decryption is possible​​​​​ S413, the perception platform SP uses a partial private key based on the Paillier TD cryptosystem and partial decryption get decrypted and calculates and finally sends these data to the cloud computing service provider CSP; S414, after the cloud computing service provider CSP receives the data, uses a partial private key based on a Paillier TD cryptosystem After partial decryption, the decrypted , and based on The decrypted intermediate parameters ; S415、if then the intermediate parameter ; Otherwise, ; cloud computing service provider, CSP, computes intermediate parameters i.e. and sends to the perception platform, SP; In S416, the perception platform SP calculates the absolute value of the two numbers under the ciphertext, and obtains ; S417, the sensing platform SP and the cloud computing service provider CSP repeat the above steps S411-S416 together to calculate , and finally multiply and to obtain .

5. The method of claim 4, wherein, In step S4, the perception platform SP, the cloud computing service provider CSP and the task participant use a secure division protocol SDIV to obtain the time consumed by the task participant to reach the task location, and the specific steps of SDIV are as follows: S421、The perception platform SP randomly selects Parameters of bits , based on the ciphertext under the addition and scalar multiplication of intermediate parameters That is ; S422, the perception platform SP uses a partial private key based on the Paillier TD cryptosystem and intermediate parameters obtains the decryption and sends to the CSP; S423, the cloud computing service provider CSP uses the partial private key based on the Paillier TD cryptosystem and the intermediate parameter After that, the decryption is obtained, and based on The intermediate parameter is completely decrypted , and finally it is sent to the task participant ; S424, task participant Possessing the perceived speed of the task , calculate the intermediate parameters After calling the encryption function based on the Paillier TD cryptosystem, get And send it to the perception platform SP; S425, finally, the perception platform SP computes the required time for the task participant to get from the current location to the task location .

6. The method of claim 5, wherein, In step S4, the participants in the SP computing task of the perception platform Remuneration and the value it provides to society The specific steps are as follows: S431, the sensing platform SP calculates the time needed to collect the sensing data ; S432, based on the Paillier TD cryptosystem, the sensing platform SP uses a partial private key and partially decrypts and sends to the CSP; S433, the cloud computing service provider CSP uses the partial private key and decrypts , obtaining a partial decryption and based on , fully decrypts the time needed for the task participant to collect the perception data and sends it to the perception platform SP; S434、Assuming the efficiency of the task participant in collecting the perception data is fixed, then the utility of the task participant is proportional to the time spent collecting the perception data, specifically: ; S435、The sensing platform SP calculates the reward for the task participant to collect the sensing data and the social value provided by the task participant according to the utility of each task participant , and the specific formula is: , , wherein is a hyperparameter used to describe the relationship between the utility and the reward / value.

7. The method of claim 6, wherein, In step S4, the perception platform SP realizes the two goals of maximizing the social welfare and the income of the requester based on a dynamic programming algorithm, and the specific steps are as follows: S441、The perception platform SP initializes a dynamic planning table , size is , where n is the number of task participants, is the task budget of the task requester; S442, iterate over all task participants i and current task budget j, if the current task participant's reward does not exceed the task budget, i.e. then the maximum social value of selecting or not selecting the task participant i needs to be calculated, i.e. ; S443, if the reward of the current task participant exceeds the task budget, i.e. , only the task participant i can be selected, i.e. ; S444, the sensing platform SP creates a list of selected participants, traverses all the task participants from the last task participant, judges whether is equal to , if equal, it means that the task participant i is selected, then the task participant i is added to the list of selected task participants, and the remaining budget of the task is updated, i.e. the task budget is reduced by the reward of the task participant i, until the task budget is 0.

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