CN112291354A - Privacy protection method for participants of crowd sensing MCS based on block chain - Google Patents
Privacy protection method for participants of crowd sensing MCS based on block chain Download PDFInfo
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Abstract
A privacy protection method for participants of a crowd sensing MCS based on a block chain. Firstly, the invention utilizes the dispersed structure and the consistency method of the block chain and combines the SM9 public key encryption algorithm to ensure the safe anonymous identity authentication of the participants and the non-falsification of the perception data. Secondly, in order to protect the position privacy of the participants and improve the task allocation rate, a participant pre-registration method is provided, the hidden area replaces the specific position of the participants, and an optimal participant-task set is solved by using a greedy algorithm. Finally, the method is based on the environment design of the linux operating system and is realized by using Go language, and the privacy protection method provided by the invention is realized. Experimental results prove the feasibility and the availability of the method provided by the invention.
Description
Technical Field
The invention belongs to the technical field of networks, and particularly relates to a block chain-based crowd sensing participant privacy protection method.
Background
A typical MCS system consists of a data requester, a server (MCS platform) and a mobile user. The server distributes the tasks of the data requesters to the mobile users in the MCS system, and the mobile users use the mobile intelligent equipment to complete data acquisition and send back to the server and obtain certain rewards.
With the advent of the intelligent era, crowd sensing[1，2]The method comprises the following steps that (Mobile crown Sensing, MCS for short) Mobile equipment with sensors is used for collecting and sharing data, participants use the MCS as a new Sensing environment platform and register in the new Sensing environment platform to participate in a Sensing task, a server selects proper participants (Mobile equipment) to complete the data collection task, and the participants upload data meeting conditions to obtain rewards.
To accomplish the crowd sensing task, the participants need to physically go to the server-specified location to collect the required data. During this period, the identity and location information of the participants may be revealed to an untrusted server, in which: 1) participants need to submit their positions to a server so as to more effectively distribute tasks, and attackers can maliciously acquire the position information of the participants; 2) when a participant accepts an assigned task, the server knows the final destination of the participant, and an untrusted server also reveals the location information of the participant; 3) when the server processes the payment after the task is completed, the identity information of the participant and even the work place can be deduced by linking the payment amount with the reward. Therefore, the enthusiasm of participants is greatly reduced, and the perception task completion rate is reduced. Therefore, how to protect the privacy of the participants is crucial to the crowd sensing system.
To address the above challenges, and to meet the requirements of both participant location privacy and identity information protection, a blockchain may be introduced as a trusted third party.
There have also been many related studies in recent years regarding methods for location privacy protection. The k-anonymity method is adopted to improve the privacy security of the participants as in document [3], namely: the position of the participant is hidden under k-1 other participants, so that the position privacy protection of the participant is achieved. Documents [4, 5] use temporal and spatial stealth techniques to mask the position of a participant's stealth area at a specific time to meet privacy requirements. Document [6] proposes a cluster-based approach in which the server assigns tasks to a cluster head, rather than to cluster members. However, these countermeasures only consider the location privacy protection of the participants, and even if the location information has the strongest protection in the task allocation process, the identity information of the participants can be revealed in the task payment process.
Some researchers have attempted to apply blockchain techniques to crowd sensing tasks[8-9，13]Current work rarely considers the identity privacy and anonymity of participants while considering location privacy protection. Such as document [14 ]]A framework is proposed to allow workers to generate their pseudonyms from their device ids, but this protocol sacrifices worker responsibility, as workers can forge pseudonyms without proving themselves bound to the true identity, which gives malicious workers the opportunity to reward counterfeit identities. Document [15]]The identity authentication scheme under the cloud environment realizes double-factor authentication of 'password + secret key' through a certificateless public key cryptosystem, guarantees authenticity of identity authentication of participants, and increases system burden because an authentication node server needs to be ensured to be online. Document [16]]It is pointed out that even in the alliance chain Fabric which adopts the two-stage security certificate system to realize identity authentication, the requirement of real-name and controllable anonymity of the participant identity can be realized. However, the original transaction certificate generation scheme in the Fabric has a large amount of calculation, a complicated certificate structure and weak supervision capability.
In blockchain networks, where participants use a cryptocurrency transaction service, each participant is associated with anonymous account information, it is difficult for other participants of the MCS system to determine the true identity of the account owner. As shown in fig. 1, after the participant sends the transaction request, the collected data is packaged into a new data block, and hash operation is performed to obtain a hash value, thereby ensuring the security of the data. After the consensus verification of other nodes confirms that a new data block is obtained, the confirmation of six blocks is required to be obtained and then the data block can be written into the block chain, and once the data block is written into the block chain, the data block cannot be tampered. The outer block chain has the characteristics of decentralization, transparency and the like[7]Each block contains some messages submitted by the network nodes and is verified by the entire network according to a predefined agreement protocol, ensuring reliable delivery of the messages over untrusted networks.
Disclosure of Invention
Under the development of mobile handsets and crowd sensing applications, mobile devices are used to seamlessly sense, collect and transmit information. The participants move to a designated location to collect data and then send the data to the requester for a reward. However, under the traditional crowd sensing framework that relies on a centralized server, the identity information of the participants is exposed to the server when registering and obtaining the reward; the position is easily obtained by an attacker in the process of collecting the sensing data, and the position privacy cannot be effectively protected.
Aiming at the problem, the invention provides a privacy protection method for participants of a crowd sensing MCS based on a block chain, wherein the roles of the crowd sensing MCS comprise the participants, requesters, data service providers and agents. During the execution process of the crowd sensing MCS, the participants:
1) and (3) authenticating the identity of the participant:
before applying for a task, a participant applies for authentication to an identity authentication service to generate an identity identifier and a secret key; storing the authorized content subjected to identity authentication in a local database of the data service provider;
2) the requester issues a task:
the task requester issues a sensing contract containing task information and execution requirements of the participants to a public block chain in a transaction form by using the signatures and public keys of the participants; the agent downloads all information related to the task from the public blockchain and publishes the information to a private blockchain network of the agent;
3) the participants apply for the task:
after receiving the broadcast task information, the participants who want to execute the task initiate a contract containing the work information and deposit to sign the corresponding task according to the requirement; deposit is used to prevent fraud, if the participant does not successfully perform identity authentication, the deposit is returned;
4) participant task allocation:
selecting participants to perform task allocation; the participant selected to be assigned with the task enters step 5); the deposit of the non-selected participant is returned; selecting participants by adopting a space position protection and optimization method based on a greedy algorithm to select optimal participant-tasks;
5) uploading perception data by the participants:
the participants upload the collected data to the blockchain; the agent verifies the quality of the uploaded data;
6) and (4) payment:
if the uploaded data is qualified, the requester defines an intelligent contract in the blockchain to automatically perform the payment process.
In the above process:
firstly, the invention creates a method for utilizing the dispersed structure and consistency of the block chain and combining an encryption algorithm (such as an SM9 public key encryption algorithm) to ensure the safe anonymous identity authentication of participants and the non-falsification of perception data. Secondly, in order to protect the position privacy of the participants and improve the task allocation rate, a participant pre-registration method is provided, and the hidden area is used for replacing the specific position of the participants to obtain an optimal participant-task set (by using a greedy algorithm).
The privacy protection method based on the fabric platform design is realized, and experimental results prove the feasibility and the usability of the method.
Drawings
FIG. 1 is a diagram of a block chain transaction process in the prior art;
FIG. 2 is a block chain based MCS system framework diagram
FIG. 3 is a task allocation diagram of a distance-based travel cost model;
FIG. 4 is a time chart of the uplink of identity information ciphertext;
fig. 5(a) and 5(b) are graphs of pre-registration success rate and time cost at different Q, P, where:
fig. 5(a) is where Q is 2, P is increased; fig. 5(b) is P ═ 6, Q is increased;
FIG. 6 is a graph of average run time for tile generation;
FIG. 7 is a graph of the results of task allocation success rates at different maximum travel distances.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and detailed description, wherein the following is arranged in a manner that facilitates:
1 overview of the method
The invention provides a participant privacy protection method of a block chain-based crowd sensing MCS, which introduces the block chain as a trusted third party. In order to prevent the re-identification attack, the private blockchain is used for dispersing the transaction records of the participants, and the transaction records of the participants are difficult to be damaged by an attacker. The block chain technology is combined with the identity authentication of the SM9 public key encryption algorithm, so that the safe and anonymous registration of the participants is realized, and the identity information of the participants is protected. In order to establish trust between a participant and a requester and improve the position privacy protection of the participant, the invention uses a greedy algorithm and trains a participant-task model which can output the optimal output by reducing the stealth area of the participant.
The experimental result shows that the privacy protection method provided by the invention can select the optimal participant-task set for the system in the MCS system for providing participant privacy protection, so that the high allocation rate of the task is ensured, and the win-win situation of the participant and the request is achieved.
2 System model
As shown in fig. 2, the overall MCS system framework mainly includes the following 4 roles: participants, requesters, data facilitators, agents.
The participants perform the following procedure in the block chain based MCS:
1. and (3) authenticating the identity of the participant: before applying for a task, a participant needs to apply for authentication from an identity authentication service to generate an identity identifier and a secret key.
The participant can automatically update the key, during which the identity identifier remains unchanged as a public key. The private key is kept by the participant himself.
The invention uses SM9 public key encryption algorithm to encrypt participant identity information, the authorized content after identity authentication is stored in the local database of data service provider, and the data access program can find out the corresponding field on the block chain for inquiry according to the account information encrypted by the participant public key.
2. The requester issues a task: the task requester publishes a sensory contract containing task information and participant performance requirements to the public blockchain in the form of a transaction using its signature and public key. The agents download all information related to the task from the public blockchain and publish it on their private blockchain networks. To maintain consistency of task information on two blockchain networks (public chain and their own private chain), the agent needs to create a new intelligent contract[17]To ensure that each task is successfully allocated without losing the cryptocurrency deposit.
3. The participants apply for the task: upon receiving the broadcasted task information, the participants who want to perform the task may initiate a contract containing the work information (identity certificate, preferred work area) and a certain deposit (gasoline fee). The deposit is used to prevent fraud and is returned if the participant fails to authenticate. If pre-registration is successful, the participant will participate in the final selection process. The invention sets control parameters P and Q to improve the quality and the pre-registration success rate of the participants.
4. And (3) task allocation: in order to protect the position privacy of the participants, a space position protection and optimization method based on a greedy algorithm is adopted to narrow the stealth area and select an optimal participant-task set. The selected participant collects perception data and if the participant is not selected as the last participant, the corresponding deposit is returned.
5. Uploading perception data: the participants upload the collected data to the blockchain using the SM9 encryption algorithm. The agent (miner) verifies the quality of the uploaded data. Considering the storage space problem of the blockchain, the invention only uploads (identity information) metadata, and the actual data is stored in the distributed database.
6. And (4) payment: if the uploaded data is qualified, the requester defines an intelligent contract in the blockchain to automatically perform the payment process.
2.1 models and assumptions
The invention provides participant identity information protection and position privacy protection by considering the block chain technology, reduces the preferred stealth area of the participant, obtains the optimal worker set and ensures the distribution rate of the perception task.
TABLE 1 symbols used in the creation of the invention
The symbols used in the creation of the present invention are shown in table 1. i is the participant's serial number and j is the task serial number.
2.2 associated definitions
In the crowd sensing system, the invention defines the set of participants as W ═ W1，w2，...，wiThe set of tasks released by the requester is T ═ T1，t2，...，tj}. Designing a privacy protection method: at participant wiIn the process of participating in crowd sensing, the identity information and the position information of the crowd sensing are protected in privacy, and the task t is improved as much as possiblejThe distribution ratio of (c).
To analyze this problem, the present invention is defined as follows:
definition 1: (participant preferred work area ri< o, r >) to protect the participants' real locations, the participants need only upload their preferred work areas, not their specific locations. Wherein o is riR is a radius.
Definition 2: (participant stealth region<ai，fi>)aiIs a spatially anonymous region based on the actual location of the participant, fiIs a probability density function.
Definition 3: (target sub-area j) in order to cover the task to the maximum extent, the invention divides the task area into a plurality of sub-areas, and assuming that the working area of the participant is circular, the divided space is integral multiple of the radius of the working coverage area of the participant. Thereby converting the full coverage of the target area approximately into the full coverage of the sub-targets in the sub-area.
Definition 4: (optimal coverage set problem) define the participant-task coverage set as the set S ═ w1，w2，...，wi，t1，t2，...，tjSet C { { w { }i，tj}wi∈S，tjE S is a subset of S, and a minimum subset of S, a e C, is found to cover S.
Definition 5: (task Allocation Rate TA) whether the task selected participant wiCan be described as wiMapping to sub-region j, represented by matrix X, if X i，j1, then it means that the sub-region j ∈ M selects participant wi(i∈N)。
3 privacy protection method based on block chain
3.1 participant anonymous identity authentication based on SM9 Algorithm
Before applying for a crowd sensing task, a participant needs to authenticate with an identity authentication service center. In order to protect the encrypted storage of the participant identity characteristic information, the invention creates that the SM9 public key encryption algorithm is used for encrypting the participant identity information. As shown in Algorithm 1, assume that the message to be sent is a string of bits M, ppub-eLen is the bit length of M, KDF () is a key derivation function, ENK () is a block cipher algorithm, MAC () is a keyed message authentication code, and e () is a bilinear pair.
By performing SM9 encryption operation on the participant identity information, the identity of the participant and the relevance between different transactions of the same participant are hidden. And respectively generating digital abstracts by using the account of the participant and the plurality of identity characteristic information of the participant through an SM9 algorithm, and connecting the digital abstracts into character strings by using the well number combination to generate the digital abstracts of the participant. Similarly, the public key of the data service provider is used for carrying out public key encryption on the data service provider character string combination to form a data service provider public key encryption ciphertext, then the participant digital abstract and the data service provider digital abstract are combined to form the participant public key encryption ciphertext, and the combined ciphertext is written into a block chain (uplink storage), so that the authorized content is written into the block chain and is not tampered.
3.2 participant apply for task phase
Any participant may upload their work information and pay a certain subscription to contract with the task requester in order to be selected to complete the task. Thus, the number of participants in the contract may exceed the number of workers actually required.
Due to the differences in geographic areas, most participants may only be present in certain sub-areas of interest, and some remote sub-areas may not be covered by participants, which may not reach the goal of task coverage.
Data quality is also degraded due to duplication of hot spot data and lack of remote data.
Therefore, to prevent excessive coverage of the hot sub-area, the present invention proposes a pre-control algorithm for the participants, as shown in algorithm 2. The algorithm considers the preferred working area of the participants and the global task coverage target, and sets control parameters P and Q, wherein P is the maximum number of the participants required by the task requester, and Q is a multiple of the task coverage target. Equation (1) shows that if a participant who chooses to work in a certain area exceeds P, then a participant who chooses to work later in the area cannot successfully contract.
Wherein xi，jRepresenting the selection participant matrix, xi，jWhen 1, participant wiSelecting a child region j; x is the number ofi，jsi，jA work area representing a selection participant; coverage rate target g of task coverage is equal to [0, 1 ]]。
3.3 participant task assignment phase
The requester puts task tjAnd the data is issued to a block chain, the participants upload the preferred working areas of the participants when applying for tasks, and the position anonymity of the participants is ensured by using the stealth areas. However, the anonymity of the position brings uncertainty of sensing task distribution, and the over-protection of the position privacy causes low distribution of the task. Therefore, the invention proposes a distance-based travel cost model in combination with a greedy algorithm based on the partial set coverage problem, as shown in fig. 3. According to the model, the Euclidean distance is defined as the perception cost between the participants and the sub-region targets, the optimal participant-task set is selected while the position privacy of the participants is protected, the optimal allocation of tasks is achieved, and the allocation rate of the tasks is improved.
For the participant selection results based on the task dividing sub-region set, the invention considers the influence of the task applicant on the sub-region task and the expected travel distance of the participant to adjust the finally selected participant.
In formula (2), in the participant set, when x isi，jTraverse the other participants in the sub-area if 1
So that sm，j＜si，jThen sm，j←1，si，jWen No. 0, exchangeThe selected participant. And the result of the optimization selection of the global participants is ensured to be closer to the actual coverage target on the basis of local optimization. i denotes the travel budget for which no assignment is made if the task exceeds the participant's travel budget.
For a participant-task set, participant w is first computediTarget probability p that a subregion can be reachedi，j。
As shown in fig. 3, the hidden area is reduced to a by adopting the method of reducing the hidden areaiI.e. the working area of the participant is reduced from its own preferred working area ri to the overlapping area a of the expected travel distance area of the task and the preferred working area of the participantiCombined with a probability density function fiCalculating the stealth area aiProbability of included participants pi，j。
Based on the probability pi，jCalculating the expected distance d between the stealth area and the target taski，j。
Wherein ljIndicates the position of the target region j in the partition and dis indicates the euclidean distance function.
The invention combines a greedy strategy to distribute tasks, selects the most appropriate participant to work in the sub-region through algorithm iteration to obtain an appropriate participant-task set, updates the coverage rate of the sub-region target in real time, stops iteration (algorithm convergence) once the coverage target is reached or the travel budget of the staff is exhausted, and leads the participant w to workiThe cost-effective description of completing task j is φi，j。
Wherein the molecule di，jRepresenting the expected distance, the denominator representing the expected coverage, u representing the matrix vector of the current covered portion of the sub-region target, k representing the coverage requirement vector, if the sub-region j is completely covered, u representing the coverage requirement vectorjThe value corresponding to the sub-region target is 1 (the value range is [0, 1 ]]) Epsilon to avoid overflow.
In addition to the distance factor, rewards are also two important factors that influence the participant selection task. The invention improves the success rate of task allocation by allocating different rewards to the tasks.
Is a hyperbolic tangent function[22]For mapping probabilities to [0, 1 ]]In the scope of the invention, the invention models the task allocation rate TA as cost-benefit φi，jAnd awards
As a function of (c).
Equation (6) indicates that a less cost-effective and higher rewarding task will be selected with a high probability, and will not be selected even if the reward is high if the distance to the task exceeds the maximum travel distance D of the participant. In addition, according to the rules defined by the reward of the requester, the participant can only select the task to be completed in the preferred work area or the isolated task, thereby ensuring the benefit of the requester and achieving the win-win situation of the participant and the requester.
3.4 participant upload data and Payment
After the participants complete the task, sensory data are uploaded to the blockchain. If the time required for the task exceeds the required time, the contract is terminated and the termination request fails. If the participant uploads the data on time. If the uploaded perception data meets the requirements defined by the requester (i.e., the data is qualified), the participant is rewarded and refunds the deposit; if the uploaded sensory data is not satisfactory (i.e., the data is not acceptable), the participant is not rewarded and the deposit is transferred to the agent.
Unlike traditional payment methods, in a blockchain network, the participant's account information is not linked to any identity information.
The smart contract automatically pays a participant reward in cryptocurrency if the data meets the applicant-defined requirements using zero knowledge proof to verify the eligibility of the data.
4 evaluation of Performance
In this example, the underlying platform used is built on a Linux operating system, and the software environment is jetbrans gorand 2019.2.3 x 64. The platform function is realized by using GO language, and the used data set, parameter setting, evaluation index and simulation result are introduced in the invention.
4.1 privacy Security
The invention completes the verification of the server based on the block chain characteristic to form a complete mode of 'on-chain + under-chain' and 'open + privacy', and the block chain can ensure that the privacy of the participant can not be used by any other person at will, and the use right is on the hands of the participant rather than the hands of a platform. In addition, the characteristic of block chain decentralization and non-tampering solves the single-point failure risk caused by CA centralization, and compared with the traditional PKI/CA system, the block chain decentralization and non-tampering method has obvious advantages in safety.
FIG. 4 is a time chart of the uplink of the identity information cryptograph, the uplink time is between 16 s and 155s, the number of transactions (blocks) is 1000 to 10000, and the uplink time increases with the increase of the number of transactions. The invention creates the block output time of the bottom layer block chain platform of the identity authentication which is equal to the sum of the uplink time and the final block confirmation time, and the numerical value of the result is far less than that of the block chain platforms such as the bitcoin platform, the Ethengfang platform and the like. Therefore, the block-out time of the identity authentication mechanism is short, and the uplink time is fast and stable.
4.2 Pre-registration success Rate and time cost
In order to compare the effects of the control parameters P and Q on the success rate of the pre-registration of the participants and the time cost result, the invention dynamically adjusts one parameter and fixes the other parameter. As shown in fig. 5(a), when Q is 2, the time cost increases as the P value increases, because the maximum threshold for adapting to the sub-area participant increases, and the registration success rate can reach 90% or more. However, as can be seen from fig. 5(b), when it is determined that P is 6, the Q value is increased, and due to the limitation of the number of persons in the sub-area, more participants refuse to contract, resulting in a decrease in the success rate of average registration of participants.
4.3 task Allocation Rate
The performance of the system was analyzed from the success rate of task allocation and the execution efficiency of the blockchain through a series of experiments using a composite data set. Table 2 lists the experimental parameter settings. The present invention sets the travel distance D of the participant between 2km and 10km, and sets the stealth mode as a circular area mode. The stealth area of each participant is uniformly selected within a map area of 15-35% radius, with the ideal task coverage ratio of 100% for each sub-target. A task allocation success rate (TA) was evaluated.
Table 2 experimental environment parameter settings
The running time of the tiles in the method is shown in fig. 6, and the average time for generating the tiles is increased with the number of participants, but all the time is kept in the millisecond level. The run time of tile generation is mainly affected by the number of participants in the task, since the larger the number of participants, the larger the Merkle tree in the tile chain.
Furthermore, the proposed method was compared with the other three methods. Here, the method is denoted as TA + ri + M, that is, the participants upload their preferred work areas, and the task rewards are different, where:
● TA + ri-M: the participants upload the preferred work areas of the participants and the task rewards are the same
● TA-ri + M: the participants upload their specific positions and the tasks are rewarded differently
● TA-ri-M: the participants upload their specific positions and the task rewards are the same
Participants often prefer short trips, which reduces task success, assuming that all tasks have the same reward. Therefore, the success rate of task allocation is improved by allocating different rewards to the tasks. In addition, the distance traveled by the participants determines their cost effectiveness, which is also an important factor affecting the assignment of tasks. Fig. 7 shows the results of task allocation success rates at different maximum travel distances. Clearly, TA increases with increasing D because more tasks are not completed if D is small. Furthermore, it was observed that the method of different rewards performed all tasks better than the same method of rewards regardless of the value of the maximum travel distance, as higher rewards would encourage employees to select tasks that are further away. It was found that the use of hidden areas does not reduce TA compared to the use of specific location information, which means that the use of hidden areas does not affect the task allocation.
5 summary of the invention
The invention provides a privacy protection method of crowd sensing participants based on a block chain, which achieves double protection of identity information safety of the participants during registration and position privacy safety in a task process. The core idea is as follows: by introducing an identity authentication mechanism, two kinds of tension relations on the block chain are optimized. One is the conflict between the confidentiality of the participant identity information and the blockchain transparency, and the other is the contradiction between the anonymity and accountability of the participant. In the task allocation stage, in order to ensure the allocation rate of tasks and reduce the possibility of disclosing the position privacy of participants, the invention adopts a task allocation mode of TA + ri + M, and improves the task allocation rate as much as possible while ensuring the position privacy of the participants by reducing the stealth area. Experimental results show that the privacy protection method provided by the invention can well solve the problems of user identity information and position privacy and has good efficiency in improving the task allocation rate.
Reference to the literature
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Claims (7)
1. A privacy protection method for participants of crowd sensing MCS based on block chains, wherein the roles of the crowd sensing MCS comprise participants, requesters, data service providers and agents, and the method is characterized in that the participants perform the following steps in the execution process of the crowd sensing MCS:
1) and (3) authenticating the identity of the participant:
before applying for a task, a participant applies for authentication to an identity authentication service to generate an identity identifier and a secret key; storing the authorized content subjected to identity authentication in a local database of the data service provider;
2) the requester issues a task:
the task requester issues a sensing contract containing task information and execution requirements of the participants to a public block chain in a transaction form by using the signatures and public keys of the participants; the agent downloads all information related to the task from the public blockchain and publishes the information to a private blockchain network of the agent;
3) the participants apply for the task:
after receiving the broadcast task information, the participants who want to execute the task initiate a contract containing the work information and deposit to sign the corresponding task according to the requirement; deposit is used to prevent fraud, if the participant does not successfully perform identity authentication, the deposit is returned;
4) participant task allocation:
selecting participants to perform task allocation; the participant selected to be assigned with the task enters step 5); the deposit of the non-selected participant is returned; selecting participants by adopting a space position protection and optimization method based on a greedy algorithm to select optimal participant-tasks;
5) uploading perception data by the participants:
the participants upload the collected data to the blockchain; the agent verifies the quality of the uploaded data;
6) and (4) payment:
if the uploaded data is qualified, the requester defines an intelligent contract in the blockchain to automatically perform the payment process.
2. The method for protecting privacy of participants of block-chain-based crowd-sourcing aware MCS according to claim 1, wherein in step 1), the participant identity information is encrypted using SM9 public key encryption algorithm, and the steps comprise:
firstly, respectively generating digital abstracts on an account of a participant and a plurality of identity characteristic information thereof by using an SM9 algorithm, and connecting the digital abstracts into character strings by using a well number combination to generate the digital abstracts of the participant; similarly, the public key of the data service provider is used for carrying out public key encryption on the character string combination of the data service provider to form a public key encryption ciphertext of the data service provider;
and then combining the digital abstract of the participant with the data service provider digital abstract well number to obtain a public key encryption ciphertext of the participant, and writing the combined ciphertext into the block chain.
3. The privacy protection method for participants in block chain based crowd sensing MCS according to claim 3, characterized in that in the steps 2-4): dividing a task area into a plurality of sub-areas; suppose participant wiThe working area of (2) is circular, the division space of the task area is participant wiIntegral multiple of the radius of the working area, thereby approximately converting the full coverage of the target area into the full coverage of the sub-targets in the sub-area; the target area is an area where a task execution position corresponding to the execution task is located.
4. The method for protecting privacy of participants in block chain-based crowd sensing MCS according to claim 3, wherein in the step 3): the method for pre-controlling the participants comprises the following steps: if the number of participants in the task area who choose to perform the task exceeds the maximum number of participants required by the task requester, the participants who choose the task later cannot successfully sign the contract.
5. The method as claimed in claim 3, wherein the task T is divided into j sub-tasks Tj；
In the step 2), the requester sends the task tjIssuing to a blockchain;
in said step 3), participant wiAt application task tjUploading the preferred working area; participant wiOnly the preferred working area r is uploaded during the task applicationiAnd using the stealth area aiAnonymizing the participant's location; r isiAn area with o as the center and r as the radius; at riIn which there is a stealth area a containing the participantsi，aiIs a spatially anonymous region based on the participant's true location;
in the step 4), the task judges the participants and finally selects the execution task tjParticipant w ofiDenoted participant-task wi，tj}；
Defining: participant set W ═ W1，w2，...，wiT ═ T, task set1，t2，...，tjH, participant-task overlay set S ═ w1，w2，...，wi，t1，t2，...，tj}; set C { { wi，tj}wi∈S，tjE is S is a subset of S; selecting an optimal participant-task, namely finding the minimum subset A of S, belonging to the C to cover S;
task tjWhether participant w is selectediIs described as wiMapping to sub-region j, represented by the selection participant matrix X; if xi，j1 then indicates that sub-region j selects participant wi；j∈M，i∈N，xi，jRepresenting the selection participants, sub-region j representing the jth sub-region, X representing the selection participant matrix, M being the number of sub-regions, N being the number of participants;
si，jrepresenting the participant coverage of task tj, D representing the participant maximum travel distance,
let participant wiTo task tjIs di，j(ii) a g is a task coverage range target;
first, participant w is calculatediProbability p that a subregion can be reachedi，j：
Adopting a method for reducing the stealth area: participant's work area from riNarrowing down to the region of expected travel distance of the task and riOverlapping region ai(ii) a Combined probability density function fiCalculating the stealth area aiProbability of included participants pi，j：
Then, based on pi，jCalculate aiAnd task tjIs expected distance di，j：
Wherein ljRepresenting the position of the sub-region j in the partition, dis representing the Euclidean distance function;
then, task allocation is carried out by combining a greedy strategy, and the most appropriate participant is iteratively selected to the corresponding sub-region through an algorithm to obtain an appropriate participant-task set; meanwhile, the coverage rate of the participants in the sub-area is updated in real time, and once the coverage target is reached or the travel budget is exhausted, the iteration is stopped;
participant wiThe cost-effective description of completing task tj is φi，j：
Wherein the denominator
Indicates the pre-coverage, ujMatrix vector, u, representing the current covered portion of the sub-area objectjHas a value range of [0, 1 ]]Kj denotes the coverage requirement vector; if sub-region j is completely covered, ujThe target corresponding value of the sub-region is 1 (u)jHas a value range of [0-1 ]]) (ii) a The parameter ε is to avoid overflow; x is the number ofi，jsi，jA work area representing a selection participant;
finally, the final selected participant is adjusted taking into account the influence of the task applicant on the sub-area task and the expected travel distance of the participant:
di，j≥0，i＝1，...，n；j＝i，...，m
in the formula, in the participant set W, when xi，jTraverse the other participants in the sub-area if 1
So that sm，j＜si，jThen sm，j←1，si，jAnd (3) the step of either differentiating the selected participants or the global participants to ensure that the result of the optimization selection of the global participants is closer to the actual coverage target on the basis of local optimization; biIndicating a travel budget for which no assignment is made if the task exceeds the participant's travel budget.
6. The method for protecting privacy of participants in block chain based crowd sensing MCS according to claim 5, wherein in the step 4), the success rate of task allocation, namely task allocation rate TA, is further increased by allocating different rewards to the tasks;
the task allocation rate TA is expressed as a cost benefit φi，jAnd awards
As a function of (a) or (b),
selecting a less cost-effective and more rewarded task with a high probability, which will not be selected even if the reward is high if the distance to reach the task exceeds the maximum travel distance D of the participant; meanwhile, the participants can only select the tasks to be completed in the preferred work area or the isolated tasks.
7. The privacy protection method for participants of block chain based crowd sensing MCS according to claim 1, wherein in the step 1), in the process of participant identity authentication, the identity identifier of the participant is kept unchanged as a public key, and the private key is saved by the participant; the participant allows automatic key updates;
the data access program finds out the corresponding field on the block chain according to the account information encrypted by the public key of the corresponding participant to inquire;
in step 2), the agent creates a new intelligent contract to ensure that each task is successfully distributed without losing the cryptocurrency deposit;
in the step 3), the working information initiated by the participant comprises an identity certificate and a preferred working area;
in the step 5), the participant only uploads the identity information metadata, and the actual data is stored in the distributed database;
in the step 5) and the step 6), after the participants finish the tasks, uploading the sensing data to the block chain:
when the time required by the task exceeds the required time, the contract is terminated, and the termination request fails;
when the participants upload data on time: if the uploaded perception data meets the requirement defined by the requester, namely the data is qualified, the participant is rewarded and refunds the deposit; if the uploaded sensory data is not satisfactory, i.e. the data is not acceptable, the participants are not rewarded and the deposit is transferred to the agent.
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