CN113222720A - Reputation-based privacy protection incentive mechanism method, device and storage medium - Google Patents

Reputation-based privacy protection incentive mechanism method, device and storage medium Download PDF

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CN113222720A
CN113222720A CN202110534099.3A CN202110534099A CN113222720A CN 113222720 A CN113222720 A CN 113222720A CN 202110534099 A CN202110534099 A CN 202110534099A CN 113222720 A CN113222720 A CN 113222720A
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李蜀瑜
李嫣然
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Shaanxi Normal University
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Abstract

The application discloses a credit-based privacy protection incentive mechanism, a credit-based privacy protection incentive device and a credit-based privacy protection incentive storage medium, relates to the technical field of crowd sensing, and solves the problem that the prior art cannot protect privacy and ensure good perceived data quality; the method comprises the following steps: acquiring a perception task uploaded by a task requester; issuing a perception task to a platform user; obtaining bidding information, including quotations generated by a platform user when receiving a perception task; obtaining credit information of each bid platform user, wherein the credit information comprises a credit value and a continuous high-grade factor; determining a winning bid user according to the bid information and the reputation information and paying a reward; acquiring sensing data and sending the sensing data to a task requester; updating the credit information of the winning bid user according to the scores; the method and the device have the advantages that the perception data with higher quality can be obtained while privacy is protected, the grades of the users are divided, and the reputation value and the continuous high-grade factors of the users can be referred when the perception data are further divided.

Description

Reputation-based privacy protection incentive mechanism method, device and storage medium
Technical Field
The application relates to the technical field of crowd sensing, in particular to a privacy protection incentive mechanism, a device and a storage medium based on credit.
Background
With the development of society, more and more electronic products are intelligentized in life, and the use frequency of smart phones is increasing in life. With the development of electronic peripheral products, more times, the smart phone is used for controlling peripheral intelligent household appliances, such as intelligent door lock control by the smart phone and control of household appliances such as air conditioners and washing machines by the smart phone. Common mobile smart devices include mobile smart phones, smart watches, tablet computers, and the like, which are capable of controlling smart appliances, and thanks to sensors integrated in the mobile smart devices, the process of using the mobile smart devices is established as network data, which is personal privacy.
Because of the value of the network data, mobile group perception is developed, a typical mobile intelligent perception system is composed of a cloud-based platform and a large number of users of mobile intelligent devices, the platform issues tasks of task requesters, and the users of the mobile intelligent devices collect and upload perception data according to the tasks. And as the perception data uploaded by the mobile intelligent user is the real cost of the user, and the real cost contains sensitive information of the mobile intelligent device user, the protection of the user's safety information is very important.
At present, the quality of the currently acquired sensing data is not high, and the sensing data provided for the mobile intelligent sensing system cannot support the requirements of task requesters.
Disclosure of Invention
The embodiment of the application provides a credit-based privacy protection incentive mechanism method, a credit-based privacy protection incentive mechanism device and a storage medium, solves the problem that the prior art cannot guarantee good quality of perception data while protecting privacy, achieves the purpose that higher-quality perception data can be obtained while protecting privacy, and can refer to the credit value and the continuous high-score factor of a user when scoring is performed on the perception data obtained by the user and grades the user.
In a first aspect, an embodiment of the present invention provides a privacy protection incentive mechanism method based on reputation, where the method includes:
acquiring a perception task uploaded by a task requester;
issuing the perception task to a platform user;
obtaining bidding information, wherein the bidding information comprises a quotation generated by the platform user after receiving the perception task;
obtaining credit information of each bid platform user, wherein the credit information comprises a credit value and a continuous high-grade factor;
determining a winning bid user according to the bid information and the reputation information;
paying the winning bid user reward;
acquiring perception data of the winning bid user, and sending the perception data to the task requester;
and obtaining the score of the task requester on the winning bid user, and updating the credit information of the winning bid user according to the score.
With reference to the first aspect, in a possible implementation manner, the obtaining reputation information of each bid-placed user of the platform includes:
judging whether each bid platform user stores the reputation value and the continuous high score factor in a platform;
if the judgment result is negative, the platform user is represented as a new user, and an initial credit value and an initial continuous high-grade factor are distributed to the platform user;
if the judgment result is yes, the platform user is represented as an old user, and the corresponding credit value and the continuous high-score factor on the platform are obtained.
With reference to the first aspect, in a possible implementation manner, the determining a winning bid user according to the bid information and the reputation information includes:
calculating the probability of each bid platform user being selected according to the reputation information and the bid information of each bid platform user;
randomly selecting one of the quotations as a winning bid quotation of the task according to the probability distribution, and adding the winning bid quotation into a winning bid quotation set;
determining the platform user having at least one bid belonging to the winning bid offer set as the winning bid user.
With reference to the first aspect, in one possible implementation manner, the method includes: when the reputation information of the winning bid user is updated according to the score, the calculation formula of the reputation value is as follows:
Figure BDA0003068983950000031
wherein ,
Figure BDA0003068983950000032
representing the total number of times the winning user bid was selected,
Figure BDA0003068983950000033
a threshold value that represents the reputation value is determined,
Figure BDA0003068983950000034
indicates that the platform user has bid the score of the first round, betalRepresenting a time decay factor of the first round based on an Ebingos forgetting curve;
the calculation formula of the continuous high score factor is as follows: η ═ 1+ f (t);
wherein t represents the number of times that the platform user continuously obtains the high score; when the user score of the platform is larger than a preset value, recording as a first high score; f (t) represents the Gompers growth curve function.
With reference to the first aspect, in a possible implementation manner, the probability that each bid-placed platform user is selected is assigned according to the reputation information and the bid information of each bid-placed platform user according to a calculation formula as follows:
Figure BDA0003068983950000041
wherein ,biIndicating that the ith has bid for the flatA price quote for the station user; r isiA reputation value representing that the ith bid has bid on the platform user; etaiA consecutive high score factor representing the ith user who has bid on the platform; q. q.siIndicating a bid for the platform user greater than zero.
With reference to the first aspect, in one possible implementation manner, the reward paid to the winning user is as follows:
Figure BDA0003068983950000042
wherein ,biAn offer representing the ith bid for the platform user; pr (Pr) ofiRepresenting the probability that the ith user who has bid on the platform is selected; bmaxA maximum of the bids indicating users who have bid on the platform.
With reference to the first aspect, in a possible implementation manner, each platform user may complete a plurality of the sensing tasks, and each sensing task may be completed by only one platform user.
In a second aspect, an embodiment of the present invention provides a privacy protection incentive mechanism apparatus based on reputation, including:
the task acquisition module is used for acquiring the perception task uploaded by the task requester;
the task issuing module is used for issuing the perception task to a platform user;
the bid information acquisition module is used for acquiring bid information, wherein the bid information comprises a quotation generated by the platform user after receiving the perception task;
the reputation information acquisition module is used for acquiring the reputation information of each bid platform user, and the reputation information comprises a reputation value and continuous high-score factors;
the bid winning user determining module is used for determining a bid winning user according to the bid information and the reputation information;
the reward payment module is used for paying the reward of the winning-bid user;
the perception data acquisition module is used for acquiring perception data of the winning bid user and sending the perception data to the task requester;
and the data updating module is used for acquiring the score of the user winning the bid by the task requester and updating the reputation information of the user winning the bid according to the score.
With reference to the second aspect, in a possible implementation manner, the reputation information obtaining module further includes a determining module, where obtaining the reputation information of each bid-placed platform user includes:
judging whether each bid platform user stores the reputation value and the continuous high score factor in a platform;
if the judgment result is negative, the platform user is represented as a new user, and an initial credit value and an initial continuous high-grade factor are distributed to the platform user;
if the judgment result is yes, the platform user is represented as an old user, and the corresponding credit value and the continuous high-score factor on the platform are obtained.
With reference to the second aspect, in a possible implementation manner, the determining a winning bid user according to the bid information and the reputation information by the winning bid user determining module includes:
calculating the probability of each bid platform user being selected according to the reputation information and the bid information of each bid platform user;
randomly selecting one of the quotations as a winning bid quotation of the task according to the probability distribution, and adding the winning bid quotation into a winning bid quotation set;
determining the platform user having at least one bid belonging to the winning bid offer set as the winning bid user.
With reference to the second aspect, in a possible implementation manner, the data updating module further includes a reputation calculating module, and a calculation formula for the reputation value is:
Figure BDA0003068983950000061
wherein ,
Figure BDA0003068983950000062
representing the total number of times the winning user bid was selected,
Figure BDA0003068983950000063
a threshold value that represents the reputation value is determined,
Figure BDA0003068983950000064
indicates that the platform user has bid the score of the first round, betalRepresenting a time decay factor of the first round based on an Ebingos forgetting curve;
the calculation formula of the continuous high score factor is as follows: η ═ 1+ f (t);
wherein t represents the number of times that the platform user continuously obtains the high score; when the user score of the platform is larger than a preset value, recording as a first high score; f (t) represents the Gompers growth curve function.
With reference to the second aspect, in a possible implementation manner, the winning bid user determining module further includes a winning bid probability calculating module, configured to allocate, according to the reputation information and the bid information of each bid platform user, a probability that each bid platform user is selected, where a calculation formula is as follows:
Figure BDA0003068983950000071
wherein ,biAn offer representing the ith bid for the platform user; r isiA reputation value representing that the ith bid has bid on the platform user; etaiA consecutive high score factor representing the ith user who has bid on the platform.
With reference to the second aspect, in a possible implementation manner, the reward payment module further includes a reward calculation module, configured to pay a reward to the winning bid user as follows:
Figure BDA0003068983950000072
wherein ,biAn offer representing the ith bid for the platform user; pr (Pr) ofiRepresenting the probability that the ith user who has bid on the platform is selected; bmaxA maximum of the bids indicating users who have bid on the platform.
With reference to the second aspect, in one possible implementation manner, each platform user in the winning bid user determining module may complete a plurality of the sensing tasks, and each sensing task may be completed by only one platform user.
In a third aspect, a server for a reputation-based privacy preserving incentive mechanism includes a memory and a processor;
the memory is to store computer-executable instructions;
the processor is configured to execute the computer-executable instructions to implement the method of any of the first aspect and the first aspect.
In a fourth aspect, a computer-readable storage medium stores executable instructions, which when executed by a computer, can be implemented to implement the method of any one of the first aspect and the first aspect.
One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:
by providing a privacy protection mechanism method, device and storage medium based on credit, the embodiment of the application adopts the perception task uploaded by the task requester, and the platform can integrate people with various requirements and provide a platform for issuing the perception task; the method comprises the steps of issuing a sensing task to a platform user, providing a issuing platform, and enabling a task requester to issue the task; obtaining bidding information, reading a credit value and a continuous high-grade factor, and determining a winning bid user, wherein the bidding information is that a platform user generates a quotation when receiving a perception task, and the bidding user is selected based on a bidding mode, so that the user with a high credit value can be selected to the maximum extent; paying the reward of the winning-bid user and a reward payment mechanism, and attracting more users with high-quality perception data and participating in perception tasks of the platform; the method comprises the steps of obtaining perception data of winning bid users and sending the perception data to a task requester, wherein the feedback of the data is that a task publisher can obtain data required by the task publisher; the method comprises the steps of obtaining the scores of a task requester on perception data, updating the reputation value and the continuous high score factor of a winning bid user according to the scores, and updating the reputation value and the continuous high score factor of the user, so that the perception data quality provided by the user and the reward obtained by the user are both positive in the whole process, the improvement of the perception data quality is promoted, more people can be stimulated to add perception tasks, the problem that the privacy cannot be guaranteed while the perception data quality is good in the prior art is solved, the perception data with higher quality can be obtained while the privacy is protected, the perception data obtained by the user is scored, the grades of the user are divided, and the reputation value and the continuous high score factor of the user can be referred when the perception data is scored again.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments of the present invention or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flowchart of a method steps for providing a reputation privacy-based protection incentive mechanism according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an apparatus for incentive mechanism based on reputation privacy protection according to an embodiment of the present application;
fig. 3 is a schematic diagram of a reputation privacy protection-based incentive mechanism server according to an embodiment of the present application.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In a first aspect, an embodiment of the present invention provides a privacy protection incentive mechanism method based on reputation, where as shown in fig. 1, the method includes:
and step S101, acquiring the perception task uploaded by the task requester.
And step S102, issuing the perception task to the platform user.
Step S103, obtaining bidding information, wherein the bidding information comprises quotations generated by the platform user when receiving the perception task.
And step S104, obtaining the reputation information of each bid platform user, wherein the reputation information comprises reputation values and continuous high-grade factors.
And step S105, determining a winning bid user according to the bid information and the reputation information.
And step S106, paying the reward of the winning bid user.
And S107, acquiring perception data of the winning bid user, and sending the perception data to the task requester.
And step S108, obtaining the score of the task requester on the winning bid user, and updating the credit information of the winning bid user according to the score.
By providing a privacy protection mechanism method, device and storage medium based on credit, the perception tasks uploaded by task requesters are acquired, and the platform can integrate people with various requirements and provide a platform for issuing the perception tasks; the method comprises the steps of issuing a sensing task to a platform user, providing a issuing platform, and enabling a task requester to issue the task; obtaining bidding information, reading a credit value and a continuous high-grade factor, and determining a winning bid user, wherein the bidding information is that a platform user generates a quotation when receiving a perception task, and the bidding user is selected based on a bidding mode, so that the user with a high credit value can be selected to the maximum extent; paying the reward of the winning-bid user and a reward payment mechanism, and attracting more users with high-quality perception data and participating in perception tasks of the platform; the method comprises the steps of obtaining perception data of winning bid users and sending the perception data to a task requester, wherein the feedback of the data is that a task publisher can obtain data required by the task publisher; the method comprises the steps of obtaining the scores of a task requester on perception data, updating the reputation value and the continuous high score factor of a winning bid user according to the scores, and updating the reputation value and the continuous high score factor of the user, so that the perception data quality provided by the user and the reward obtained by the user are both positive in the whole process, the improvement of the perception data quality is promoted, more people can be stimulated to add perception tasks, the problem that the privacy cannot be guaranteed while the perception data quality is good in the prior art is solved, the perception data with higher quality can be obtained while the privacy is protected, the perception data obtained by the user is scored, the grades of the user are divided, and the reputation value and the continuous high score factor of the user can be referred when the perception data is scored again.
In step S104, obtaining reputation information of each bid platform user includes: judging whether each bidding platform user stores a credit value and a continuous high-grade factor in the platform;
if the judgment result is negative, the platform user is represented as a new user, and an initial credit value and an initial continuous high-grade factor are distributed to the platform user.
In the application, the platform users are graded and scored based on the reputation value and the continuous high-score factor, and the new users are allocated with initial reputation values and continuous high-score factors.
In step S105, determining a winning bid user according to the bid information and the reputation information includes:
calculating the probability of each bid platform user being selected according to the credit information and bid information of each bid platform user;
randomly selecting one of the quotations as a winning bid quotation of the task according to the probability distribution, and adding the winning bid quotation into a winning bid quotation set;
determining a platform user having at least one offer belonging to the winning bid offer set as a winning bid user.
The method and the system consider that the credit value of the platform user can influence the perception data, generally, the better the credit value of the platform user is, the higher the possibility of bringing high-quality perception data to the platform is, when the user reaches the platform, the platform firstly judges whether the user is a new user, if the user is the new user, the initial credit value and the continuous high-score factor of the new user are given, and if the user is not the new user, the historical credit value and the continuous high-score factor of the user are obtained. The platform further has the historical reputation value and the continuous high-score factor of the user, and the user carries out the screening of the winning bid user firstly and then carries out the selection of the winning bid user. The winning bid user uploads the perception data, the task requester scores the perception data in terms of data quality and returns the score to the platform, and the platform updates the credit value and the continuous high score factor of the winning bid user according to the score of the task requester and stores the credit value and the continuous high score factor in the platform.
In step S105, according to the reputation information and the bid information of each bid platform user, the probability assigned to each bid platform user for being selected is calculated as follows:
Figure BDA0003068983950000121
wherein ,biAn offer representing the ith bid platform user; r isiRepresenting the reputation value of the ith bid platform user; etaiRepresenting the consecutive high score factors of the ith bid platform user.
The platform remembers and calculates the probability of each user being selected according to the differential privacy index, and selects the winning bid user according to the probability, and the specific process of selecting the winning bid user based on the differential privacy index mechanism is as follows: setting a differential privacy algorithm M, O as all possible output sets of the algorithm M, and for any adjacent data sets A and B and any subset O of O, having Pr [ M (A) epsilon O]≤exp(ε)×Pr[M(B)∈O]To realizeThe platform gives an availability function q (A, o) based on an index mechanism, a division function on a returned result can be obtained according to the q (A, o), the returned result is output randomly according to the division function, the probability Pr of the chosen winning target of the platform user meets the differential privacy of the index mechanism, q is the availability function in the index mechanism, delta q is the global sensitivity of the availability function, and the probability of the platform user winning the target of the perception task meets the following conditions:
Figure BDA0003068983950000131
after the design of differential privacy is met, others cannot deduce the real price of the user from the multiple winning results, and the privacy of the user is protected. Each platform user can complete a plurality of perception tasks, and each perception task can be completed by only one platform user. And the platform randomly selects the users as the winning bid users according to the selected probability distribution of each user, and gives the corresponding reward to the winning bid users. The reward paid to the winning bid user is as follows:
Figure BDA0003068983950000132
wherein ,biAn offer representing the ith bid platform user; pr (Pr) ofiRepresenting the probability that the ith bid platform user is selected; bmaxRepresenting the maximum of the bids of the users of the bid platform.
Before step S108, before determining a winning bid user according to the bid information and the reputation information, the method includes: when the reputation information of the winning bid user is updated according to the scores, the calculation formula of the reputation value is as follows:
Figure BDA0003068983950000133
wherein ,
Figure BDA0003068983950000134
indicate winning bid user bid quiltThe total number of times of the selection is,
Figure BDA0003068983950000135
a threshold value that represents a value of a reputation,
Figure BDA0003068983950000136
indicates the I round score, beta, of the user with the bid platformlRepresenting a time decay factor of the first round based on an Ebingos forgetting curve;
the calculation formula of the continuous high score factor is as follows: η ═ 1+ f (t); wherein t represents the number of times that the platform user continuously obtains the high score; when the user score of the platform is larger than a preset value, recording as a first high score; f (t) represents the Gompers growth curve function.
For time decay factor betalThe method is calculated based on Ebbinghaus forgetting curve, and the calculation method comprises the following steps:
Figure BDA0003068983950000141
wherein ,
Figure BDA0003068983950000142
representing the total number of times the platform user bid has been selected.
The continuous high score factor is in direct proportion to the continuous high score times of the user, the continuous high score factor eta of the platform user is 1+ f (t), t is the continuous high score times of the platform user, the platform adopts a Gompertz function to calculate the continuous high score factor according to the continuous high score times of the user, and the function f (t) is specifically as follows:
Figure BDA0003068983950000143
where K, a and b are constants, K is 0.5, a is 0.01, and b is 0.7.
In the application, a reverse auction model meeting the characteristics of the incentive mechanism can improve the participation of users, and a platform can obtain data with higher quality, wherein the characteristics of the incentive mechanism needing to be met are as follows:
(1) the calculation is valid: the mechanism is computationally efficient if it completes within the polynomial time.
(2) Individuality: the utility of each user is non-negative.
(3) Authenticity: when the bid price of the platform user participating in the bid is equal to the real cost of the platform user, the effectiveness of the platform user is maximum, and the incentive mechanism meets the reality.
The objective of the platform in (2) above is to maximize the utility of the platform
Figure BDA0003068983950000151
V (S) is platform profit, the platform profit is in direct proportion to the credibility of the winning bid user and the continuous high score factor, users with higher credibility can bring better profit to the platform, and platform users with higher continuous high score factor are more stable and can bring each stable benefit to the platform.
In the above (2), when the bid price of the platform user participating in the bid is equal to the real cost thereof, the utility thereof is maximized. The utility calculation formula of the user is as follows:
Figure BDA0003068983950000152
wherein ,piIn order to win the reward of the subscriber,
Figure BDA0003068983950000153
the user's stated cost of the perceived task is won.
The embodiment of the invention provides a privacy protection incentive mechanism device based on reputation, which comprises: the system comprises a task acquisition module 201, a task publishing module 202, a bid information acquisition module 203, a reputation information acquisition module 204, a bid winning user determination module 205, a reward payment module 206, a perception data acquisition module 207 and a data updating module 208.
And the task obtaining module 201 is configured to obtain the perception task uploaded by the task requester.
And the task issuing module 202 is used for issuing the sensing task to the platform user.
And the bid information acquisition module 203 is used for acquiring bid information, wherein the bid information comprises a quote generated by the platform user when the platform user receives the perception task.
The reputation information obtaining module 204 further comprises a judging module. Reputation information acquisition module 204 is configured to acquire reputation information of each bid platform user, where the reputation information includes a reputation value and a continuous high score factor. The judgment module is used for acquiring the credit information of each bid platform user, and comprises the following steps: judging whether each bidding platform user stores a credit value and a continuous high-grade factor in the platform; if the judgment result is negative, the platform user is represented as a new user, and an initial credit value and an initial continuous high-grade factor are distributed to the platform user; if the judgment result is yes, the platform user is represented as an old user, and the corresponding credit value and the continuous high-score factor on the platform are obtained.
The winning bid user determining module 205 further comprises a winning bid probability calculating module. Each platform user can complete a plurality of perception tasks, and each perception task can be completed by only one platform user. The winning bid user determining module 205 is configured to determine a winning bid user according to the bid information and the reputation information, and includes: calculating the probability of each bid platform user being selected according to the credit information and bid information of each bid platform user; randomly selecting one of the quotations as a winning bid quotation of the task according to the probability distribution, and adding the winning bid quotation into a winning bid quotation set; a platform user having at least one offer belonging to the set of winning bid offers is determined as the winning bid user. The winning bid probability calculation module is used for allocating the probability of being selected to each bid platform user according to the credit information and the bid information of each bid platform user, and the calculation formula is as follows:
Figure BDA0003068983950000161
wherein ,biAn offer representing the ith bid platform user; r isiRepresenting the reputation value of the ith bid platform user; etaiA continuous high score factor representing the ith bid platform user; q. q.siIndicating a bid platform user with an offer greater than zero.
Reward payment module 206, further comprising a reward payment module. The reward payment module 206 is used for paying the reward of the winning-bid user; the reward payment module is used for paying the reward to the winning bid user as follows:
Figure BDA0003068983950000162
wherein ,biAn offer representing the ith bid platform user; pr (Pr) ofiRepresenting the probability that the ith bid platform user is selected; bmaxRepresenting the maximum of the bids of the users of the bid platform.
And the perception data acquisition module 207 is used for acquiring perception data of the winning bid user and sending the perception data to the task requester.
The data update module 208 also includes a reputation computation module. The data updating module 208 is configured to obtain the score of the user winning the bid, and update the reputation information of the user winning the bid according to the score. The reputation calculation module is used for calculating a reputation value according to the following formula:
Figure BDA0003068983950000171
wherein ,
Figure BDA0003068983950000172
indicating the total number of times the winning bid user bid was selected,
Figure BDA0003068983950000173
a threshold value that represents a value of a reputation,
Figure BDA0003068983950000174
indicates the I round score, beta, of the user with the bid platformlRepresenting a time decay factor of the first round based on an Ebingos forgetting curve;
the calculation formula of the continuous high score factor is as follows: η ═ 1+ f (t);
wherein t represents the number of times that the platform user continuously obtains the high score; when the user score of the platform is larger than a preset value, recording as a first high score; f (t) represents the Gompers growth curve function.
In the above modules, the platform first obtains the perception task uploaded by the task requester through the task obtaining module 201, then releases the perception task to the platform through the task releasing module 202, the platform user obtains the perception task released on the platform through the bidding information obtaining module 203, and bids for the task interested by the platform user to generate quotation, the platform obtains the platform user interested in the perception task, and obtains the reputation information of the platform user who has bid through the reputation information obtaining module 204, the reputation information includes reputation value and continuous high score factor, the winning bid user is determined through the winning bid user determining module 205 for the reputation information and the bidding information, the reward due of the winning bid user is calculated through the reward payment module 206 and is paid to the winning bid user, meanwhile, the platform obtains the perception data of the winning bid user through the perception data obtaining module 207, and sending the data to the task requester, acquiring the score of the task requester on the perception data of the winning bid user in the data updating module 208, and updating the reputation value information of the winning bid user according to the score.
Although the present application provides method steps as described in an embodiment or flowchart, additional or fewer steps may be included based on conventional or non-inventive efforts. The sequence of steps recited in this embodiment is only one of many steps performed and does not represent a unique order of execution. When an actual apparatus or client product executes, it can execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) according to the methods shown in this embodiment or the figures.
The apparatuses or modules illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. The functionality of the modules may be implemented in the same one or more software and/or hardware implementations of the present application. Of course, a module that implements a certain function may be implemented by a plurality of sub-modules or sub-units in combination.
The application also provides a server of the privacy protection incentive mechanism based on the reputation, which comprises a memory 301 and a processor 302;
the memory 301 is used to store computer executable instructions;
processor 302 is configured to execute computer-executable instructions to implement a method for a reputation based privacy preserving incentive mechanism.
The storage medium includes, but is not limited to, a Random Access Memory (RAM), a Read-Only Memory (ROM), a Cache, a Hard Disk (Hard Disk Drive), or a Memory Card (HDD). The memory may be used to store computer program instructions.
The application also provides a computer-readable storage medium, which is characterized in that the computer-readable storage medium stores executable instructions, and the computer can realize the method for realizing the privacy protection incentive mechanism based on the reputation when executing the executable instructions.
The methods, apparatus or modules described herein may be implemented in a computer readable program code means for a controller in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, Application Specific Integrated Circuits (ASICs), programmable logic controllers and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
Some of the modules in the apparatus described herein may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary hardware. Based on such understanding, the technical solutions of the present application may be embodied in the form of software products or in the implementation process of data migration, which essentially or partially contributes to the prior art. The computer software product may be stored in a storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to perform the methods described in the various embodiments or portions of the embodiments of the present application.
The embodiments in the present specification are described in a progressive manner, and the same or similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. All or portions of the present application are operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, mobile communication terminals, multiprocessor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications or substitutions do not depart from the spirit and scope of the present disclosure.

Claims (10)

1. A reputation-based privacy-preserving incentive scheme method, comprising:
acquiring a perception task uploaded by a task requester;
issuing the perception task to a platform user;
obtaining bidding information, wherein the bidding information comprises a quotation generated by the platform user after receiving the perception task;
obtaining credit information of each bid platform user, wherein the credit information comprises a credit value and a continuous high-grade factor;
determining a winning bid user according to the bid information and the reputation information;
paying the winning bid user reward;
acquiring perception data of the winning bid user, and sending the perception data to the task requester;
and obtaining the score of the task requester on the winning bid user, and updating the credit information of the winning bid user according to the score.
2. The method of claim 1, wherein obtaining reputation information for each of the bid-ed platform users comprises:
judging whether each bid platform user stores the reputation value and the continuous high score factor in a platform;
if the judgment result is negative, the platform user is represented as a new user, and an initial credit value and an initial continuous high-grade factor are distributed to the platform user;
if the judgment result is yes, the platform user is represented as an old user, and the corresponding credit value and the continuous high-score factor on the platform are obtained.
3. The method of claim 1, wherein determining winning users based on the bid information and the reputation information comprises:
calculating the probability of each bid platform user being selected according to the reputation information and the bid information of each bid platform user;
randomly selecting one of the quotations as a winning bid quotation of the task according to the probability distribution, and adding the winning bid quotation into a winning bid quotation set;
determining the platform user having at least one bid belonging to the winning bid offer set as the winning bid user.
4. The method of claim 1, comprising: when the reputation information of the winning bid user is updated according to the score, the calculation formula of the reputation value is as follows:
Figure FDA0003068983940000021
wherein ,
Figure FDA0003068983940000022
representing the total number of times the winning user bid was selected,
Figure FDA0003068983940000023
a threshold value that represents the reputation value is determined,
Figure FDA0003068983940000024
indicates that the platform user has bid the score of the first round, betalRepresenting a time decay factor of the first round based on an Ebingos forgetting curve;
the calculation formula of the continuous high score factor is as follows: η ═ 1+ f (t);
wherein t represents the number of times that the platform user continuously obtains the high score; when the user score of the platform is larger than a preset value, recording as a first high score; f (t) represents the Gompers growth curve function.
5. The method of claim 3, wherein the probability assigned to each of the bid-ed platform users being selected based on the reputation information and the bid information of each of the bid-ed platform users is calculated by the following formula:
Figure FDA0003068983940000031
wherein ,biAn offer representing the ith bid for the platform user; r isiA reputation value representing that the ith bid has bid on the platform user; etaiA consecutive high score factor representing the ith user who has bid on the platform.
6. The method of claim 1, wherein the payment paid to the winning user is as follows:
Figure FDA0003068983940000032
wherein ,biAn offer representing the ith bid for the platform user; pr (Pr) ofiIs shown asProbability that i users having bid on the platform are selected; bmaxA maximum of the bids indicating users who have bid on the platform.
7. The method of any of claims 1-6, wherein each of the platform users can perform a plurality of the awareness tasks, and each of the awareness tasks can only be performed by one of the platform users.
8. A reputation-based privacy preserving incentive mechanism apparatus, comprising:
the task acquisition module is used for acquiring the perception task uploaded by the task requester;
the task issuing module is used for issuing the perception task to a platform user;
the bid information acquisition module is used for acquiring bid information, wherein the bid information comprises a quotation generated by the platform user after receiving the perception task;
the reputation information acquisition module is used for acquiring the reputation information of each bid platform user, and the reputation information comprises a reputation value and continuous high-score factors;
the bid winning user determining module is used for determining a bid winning user according to the bid information and the reputation information;
the reward payment module is used for paying the reward of the winning-bid user;
the perception data acquisition module is used for acquiring perception data of the winning bid user and sending the perception data to the task requester;
and the data updating module is used for acquiring the score of the user winning the bid by the task requester and updating the reputation information of the user winning the bid according to the score.
9. A server for a reputation based privacy preserving incentive mechanism, comprising a memory and a processor;
the memory is to store computer-executable instructions;
the processor is configured to execute the computer-executable instructions to implement the method of any of claims 1-7.
10. A computer-readable storage medium having stored thereon executable instructions that, when executed by a computer, are capable of implementing the method of any one of claims 1-7.
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