CN113222720B - Privacy protection incentive mechanism method and device based on reputation and storage medium - Google Patents
Privacy protection incentive mechanism method and device based on reputation and storage medium Download PDFInfo
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Abstract
The application discloses a privacy protection incentive mechanism, a privacy protection incentive device and a privacy protection incentive storage medium based on reputation, relates to the technical field of crowd sensing, and solves the problem that the prior art cannot guarantee that the quality of sensed data is good while protecting privacy; the method comprises the following steps: acquiring a perception task uploaded by a task requester; issuing a perception task to a platform user; acquiring bidding information, wherein the bidding information comprises quotations generated by a platform user when the platform user receives a perception task; the credit information of each bidding platform user is obtained, wherein the credit information comprises credit values and continuous high score factors; determining winning bid users and paying according to the bid information and the credit information; obtaining perception data and sending the perception data to a task requester; updating the credit information of the winning logo user according to the score; the method and the device can obtain higher-quality perception data while protecting privacy, and divide the grades of users, and can refer to the reputation value and continuous high-score factors of the users when the perception data are marked again.
Description
Technical Field
The application relates to the technical field of crowd sensing, in particular to a privacy protection incentive mechanism, device and storage medium based on reputation.
Background
With the development of society, more and more electronic products are intelligent in life, and the use frequency of smart phones is increasingly improved in life. And along with the development of electronic peripheral products, when more, the intelligent mobile phone is used for controlling peripheral intelligent household appliances, such as intelligent door lock control by the intelligent mobile phone, and control of household appliances such as air conditioners, washing machines and the like by the intelligent mobile phone. Common mobile smart devices include mobile smartphones, smartwatches, tablet computers, etc., which are capable of controlling smart appliances, and which benefit from sensors integrated in the mobile smart devices, the process of which is established as network data belonging to personal privacy.
Because of the value of these network data, mobile group awareness has evolved, and a typical mobile intelligent awareness system consists of a cloud-based platform and a large number of users of mobile intelligent devices, and the platform issues tasks for task requesters, from which the mobile intelligent device users collect awareness data and upload the platform. Also, since the perceived data uploaded by the mobile smart user is often the real cost of the user, and the real cost often contains sensitive information of the user of the mobile smart device, it is important to protect the security information of the client.
At present, the quality of the 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 solves the problem that the prior art cannot guarantee the quality of the perceived data while protecting the privacy by providing the privacy protection incentive mechanism method, the device and the storage medium based on the reputation, realizes the protection of the privacy, can acquire the perceived data with higher quality, and can refer to the reputation value and the continuous high-score factor of the user when the perceived data is classified again because the perceived data acquired by the user is scored.
In a first aspect, an embodiment of the present application provides a reputation-based privacy-preserving incentive mechanism method, including:
acquiring a perception task uploaded by a task requester;
issuing the perception task to a platform user;
acquiring bidding information, wherein the bidding information comprises quotations generated by the platform user when the platform user receives the perception task;
the method comprises the steps of obtaining reputation information of each bidding platform user, wherein the reputation information comprises a reputation value and a continuous high score factor;
determining winning bid users according to the bid information and the reputation information;
paying the winning logo user reward;
obtaining the perception data of the winning logo user and sending the perception data to the task requester;
and obtaining the score of the task requester on the winning target user, and updating the credit information of the winning target user according to the score.
With reference to the first aspect, in a possible implementation manner, the obtaining reputation information of each bidding platform user includes:
judging whether each bidding 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 indicated to be a new user, and an initial reputation value and an initial continuous high score factor are distributed to the platform user;
and if the judgment result is yes, the platform user is an old user, and the corresponding reputation 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 bidding platform user being selected according to the reputation information and the bidding information of each bidding platform user;
randomly selecting one of the quotations as a winning bid of the task according to the probability distribution, and adding the winning bid into a winning bid quotation set;
determining said flat user having at least one bid belonging to said winning bid set of bids as said winning bid user.
With reference to the first aspect, in one possible implementation manner, the method includes: and when the credit information of the winning logo user is updated according to the score, the credit value is calculated according to the following formula:
wherein ,indicating the total number of times the winning bid user bid is selected, +.>Threshold value representing the reputation value, +.>Indicating that the platform user has been bidding for the first round of scores, beta l A time decay factor representing the first round based on the Ebinhaos 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 a high score; when the user score of the platform is larger than a preset value, marking as one-time high score; f (t) represents a Pasi growth curve function.
With reference to the first aspect, in a possible implementation manner, the calculating formula is as follows according to the reputation information and the bidding information of each bidding platform user, which are assigned to the probability that each bidding platform user is selected:
wherein ,bi Representing an ith bid for the platform user; r is (r) i Indicating that the ith bid has been bidThe reputation value of the platform user; η (eta) i Representing the ith successive high scoring factors that have been bid on the platform user; q i Representing the users of the platform that have been bid with a bid greater than zero.
With reference to the first aspect, in one possible implementation manner, the payment paid to the winning logo user is as follows:
wherein ,bi Representing an ith bid for the platform user; pr (Pr) i Representing the probability that the ith bid was made for the platform user to be selected; b max Representing the maximum of the offers that have been bid on the platform user.
With reference to the first aspect, in one possible implementation manner, each platform user may complete a plurality of the sensing tasks, and each sensing task may only be completed by one platform user.
In a second aspect, an embodiment of the present application provides a reputation-based privacy-preserving incentive mechanism apparatus, including:
the task acquisition module is used for acquiring a perceived task uploaded by a task requester;
the task issuing module is used for issuing the perceived task to a platform user;
the bid information acquisition module is used for acquiring bid information, wherein the bid information comprises quotations generated by the platform user when the platform user receives the perception task;
the reputation information acquisition module is used for acquiring reputation information of each bidding platform user, wherein the reputation information comprises a reputation value and a continuous high-score factor;
the winning bid user determining module is used for determining winning bid users according to the bidding information and the reputation information;
a reward payment module for paying the user reward of the winning mark;
the perception data acquisition module is used for acquiring the perception data of the winning logo user and sending the perception data to the task requester;
and the data updating module is used for acquiring the score of the task requester on the winning target user and updating the credit information of the winning target user according to the score.
With reference to the second aspect, in one possible implementation manner, the reputation information obtaining module further includes a judging module, and the obtaining reputation information of each bidding platform user includes:
judging whether each bidding 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 indicated to be a new user, and an initial reputation value and an initial continuous high score factor are distributed to the platform user;
and if the judgment result is yes, the platform user is an old user, and the corresponding reputation value and the continuous high-score factor on the platform are obtained.
With reference to the second aspect, in one possible implementation manner, the determining module of the winning target user according to the bid information and the reputation information includes:
calculating the probability of each bidding platform user being selected according to the reputation information and the bidding information of each bidding platform user;
randomly selecting one of the quotations as a winning bid of the task according to the probability distribution, and adding the winning bid into a winning bid quotation set;
determining said flat user having at least one bid belonging to said winning bid set of bids as said winning bid user.
With reference to the second aspect, in one possible implementation manner, the data updating module further includes a reputation calculating module, and a calculating formula for the reputation value is:
wherein ,indicating the total number of times the winning bid user bid is selected, +.>Threshold value representing the reputation value, +.>Indicating that the platform user has been bidding for the first round of scores, beta l A time decay factor representing the first round based on the Ebinhaos 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 a high score; when the user score of the platform is larger than a preset value, marking as one-time high score; f (t) represents a Pasi growth curve function.
With reference to the second aspect, in one 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 bidding information of each bidding platform user, a probability that each bidding platform user is selected, a calculation formula as follows:
wherein ,bi Representing an ith bid for the platform user; r is (r) i Representing a reputation value for an ith bid that has been bidding on the platform user; η (eta) i Representing the successive high scoring factors of the ith bid for the platform user.
With reference to the second aspect, in one possible implementation manner, the payment module further includes a payment calculation module, configured to pay the winning target user as follows:
wherein ,bi Representing an ith bid for the platform user; pr (Pr) i Representing the probability that the ith bid was made for the platform user to be selected; b max Representing the maximum of the offers that have been bid on the platform user.
With reference to the second aspect, in one possible implementation manner, each of the platform users in the winning logo user determining module may complete a plurality of the perceived tasks, and each of the perceived tasks may be completed by only one of the platform users.
In a third aspect, a server for a reputation-based privacy-preserving incentive mechanism includes a memory and a processor;
the memory is used for storing computer executable instructions;
the processor is configured to execute the computer-executable instructions to implement the method of any one of the first aspect and the first aspect.
A fourth aspect is a computer readable storage medium storing executable instructions that when executed by a computer are capable of implementing 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 application at least have the following technical effects or advantages:
the embodiment of the application provides a privacy protection mechanism method, a privacy protection mechanism device and a storage medium based on reputation, which adopt a perception task uploaded by a task requester, and a platform can integrate people with various requirements and provide a platform for issuing the perception task; issuing a perceived task to a platform user, and providing an issuing platform to enable a task requester to issue the task; acquiring bidding information, reading a reputation value and a continuous high score factor, and determining winning bidding users, wherein the bidding information is generated by a platform user when receiving a perception task, and the bidding users are selected based on a bidding mode, so that users with high reputation values can be selected to the greatest extent; the winning mark user payment and payment mechanism can attract more users with high-quality perception data and participate in the perception tasks of the platform; the method comprises the steps of obtaining perception data of winning mark users, and sending the perception data to task requesters, wherein the feedback of the data is that the task publishers can obtain data required by themselves; the method comprises the steps of obtaining the score of a task requester on the perception data, updating the credit value and the continuous high score factor of a winning target user according to the score, and updating the credit value and the continuous high score factor of the user, so that the quality of the perception data provided by the user and the reward obtained by the user are both increased in the whole process, the improvement of the quality of the perception data is promoted, more people can be stimulated to add the perception task, the problem that the prior art can not protect privacy and ensure the quality of the perception data is good is solved, the effect that the privacy is protected and the perception data with higher quality can be obtained is realized, and the user grade is divided due to the fact that the perception data obtained by the user is scored, and the credit value and the continuous high score factor of the user can be referred when the perception data is scored again.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description will briefly explain the embodiments of the present application or the drawings needed in the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for protecting incentive mechanism based on reputation privacy, which is provided by the embodiment of the application;
FIG. 2 is a schematic diagram of a reputation-based privacy protection incentive mechanism apparatus provided by an embodiment of the present application;
fig. 3 is a schematic diagram of a reputation-based privacy protection incentive mechanism server according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In a first aspect, an embodiment of the present application provides a reputation-based privacy protection incentive mechanism method, as shown in fig. 1, where the method includes:
step S101, obtaining a perception task uploaded by a task requester.
Step S102, issuing a perception task to a platform user.
Step S103, acquiring bidding information, wherein the bidding information comprises quotations generated by the platform user after receiving the perception tasks.
Step S104, the reputation information of each bidding platform user is obtained, wherein the reputation information comprises a reputation value and a continuous high score factor.
Step S105, determining winning bid users according to the bid information and the credit information.
And S106, paying out the user rewards of the winning mark.
Step S107, obtaining the perception data of the winning logo user and sending the perception data to the task requester.
And S108, obtaining the score of the task requester on the winning target user, and updating the credit information of the winning target user according to the score.
The application provides a privacy protection mechanism method, a privacy protection device and a storage medium based on reputation, which adopt a perception task uploaded by a task requester, and a platform can integrate people with various requirements and provide a platform for issuing the perception task; issuing a perceived task to a platform user, and providing an issuing platform to enable a task requester to issue the task; acquiring bidding information, reading a reputation value and a continuous high score factor, and determining winning bidding users, wherein the bidding information is generated by a platform user when receiving a perception task, and the bidding users are selected based on a bidding mode, so that users with high reputation values can be selected to the greatest extent; the winning mark user payment and payment mechanism can attract more users with high-quality perception data and participate in the perception tasks of the platform; the method comprises the steps of obtaining perception data of winning mark users, and sending the perception data to task requesters, wherein the feedback of the data is that the task publishers can obtain data required by themselves; the method comprises the steps of obtaining the score of a task requester on the perception data, updating the credit value and the continuous high score factor of a winning target user according to the score, and updating the credit value and the continuous high score factor of the user, so that the quality of the perception data provided by the user and the reward obtained by the user are both increased in the whole process, the improvement of the quality of the perception data is promoted, more people can be stimulated to add the perception task, the problem that the prior art can not protect privacy and ensure the quality of the perception data is good is solved, the effect that the privacy is protected and the perception data with higher quality can be obtained is realized, and the user grade is divided due to the fact that the perception data obtained by the user is scored, and the credit value and the continuous high score factor of the user can be referred when the perception data is scored again.
In step S104, reputation information of each bidding platform user is obtained, including: judging whether each bidding platform user stores a credit value and a continuous high score factor in the platform;
if the judgment result is negative, the platform user is indicated to be a new user, and an initial credit value and an initial continuous high score factor are allocated to the platform user.
In the application, platform users are classified and scored based on the reputation value and the continuous high score factor, and an initial reputation value and the continuous high score factor are allocated to a new user.
In step S105, the determining the winning bid user according to the bid information and the reputation information includes:
calculating the probability of each bidding platform user being selected according to the reputation information and the bidding information of each bidding platform user;
randomly selecting one of the quotations as a winning bid of the task according to the probability distribution, and adding the winning bid into a winning bid quotation set;
a user of the platform having at least one bid belonging to the winning bid set of bids is determined as a winning bid user.
The application considers that the reputation value of the platform user can influence the perception data, and in general, the better the reputation is, the greater the possibility of bringing high-quality perception data to the platform is, when the user arrives at the platform, the platform firstly judges whether the user is a new user, if the user is the new user, the new user is given an initial reputation value and a continuous high score factor, and if the user is not the new user, the historical reputation value and the continuous high score factor of the user are obtained. The platform further has the historical credit value of the user, the continuous high score factor and the quotation of the user on the perception task, and the winning mark user is screened firstly and then selected. The winning target user uploads the perceived data, the task requester scores the perceived 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 target 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, the probability assigned to each bidding platform user is selected based on the reputation information and the bidding information of each bidding platform user, and the calculation formula is as follows:
wherein ,bi Representing the bid of the ith bidding platform user; r is (r) i Representing a reputation value of an ith bid for the bidding platform user; η (eta) i Representing the successive high scoring factors of the ith bidding platform user.
The platform calculates the selected probability of each user according to the differential privacy index memorize, and selects winning target users according to the probability, and the specific process of selecting winning target users based on the differential privacy index mechanism is as follows: the differential privacy algorithm M and O are all possible output sets of the algorithm M, and for any subset O of any adjacent data sets A and B and O, pr [ M (A) [ epsilon ] O is arranged]≤exp(ε)×Pr[M(B)∈O]To achieve differential privacy, the platform gives an availability function based on an exponential mechanismThe number q (A, o), according to q (A, o) can get a branch function on the return result, finally according to the branch function output return result at random, set up the probability Pr of winning target that the platform user is selected to meet the differential privacy of the exponential mechanism, q is the availability function in the exponential mechanism, Δq is the global sensitivity of the availability function, the probability of winning target of the perception task of the platform user meets the following conditions:
after the design of differential privacy is satisfied, other people cannot infer the real price of the user from the multiple winning results, so that the privacy of the user is protected. Each platform user may accomplish multiple perception tasks, and each perception task may be accomplished by only one platform user. The platform randomly selects the users as winning target users according to the probability distribution selected by each user and gives rewards corresponding to the winning target users. The rewards paid to the winning bid users are as follows:
wherein ,bi Representing the bid of the ith bidding platform user; pr (Pr) i Representing a probability that the ith bidding platform user is selected; b max Representing the maximum of the bid amounts of the users of the bidding platform.
Prior to step S108, prior to determining the winning bid user based on the bid information and reputation information, comprising: when updating the credit information of the winning logo user according to the scores, the calculation formula of the credit value is as follows:
wherein ,indicating the total number of times the winning bid user bid is selected, +.>Threshold representing reputation value, ++>Representing the first round of scores of users of a bidding platform, beta l A time decay factor representing the first round based on the Ebinhaos 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 a high score; when the user score of the platform is larger than a preset value, the user score is recorded as a primary high score; f (t) represents a Pasi growth curve function.
For a time decay factor beta l Is calculated based on Ebbinghaus forgetting curve, and the calculation mode is as follows:
wherein ,indicating the total number of times the platform user bid was selected.
The continuous high score factor is proportional to the number of continuous high scores of the user, the continuous high score factor η= 1+f (t) of the platform user, t is the number of times the platform user continuously obtains the high score, the platform adopts a Gompertz function to calculate the continuous high score factor according to the number of continuous high scores of the user, and the function f (t) is specifically as follows:
where K, a and b are both constants, k=0.5, a=0.01, b=0.7.
In the application, a reverse auction model meeting the characteristics of an incentive mechanism is actually realized, the participation of users can be improved, and a platform is used for obtaining higher-quality data, wherein the characteristics of the incentive mechanism to be met are as follows:
(1) The calculation is effective: the mechanism is computationally efficient if it is completed within polynomial time.
(2) Individual rationality: the utility of each user is non-negative.
(3) Authenticity: the incentive mechanism satisfies the authenticity when the bidding price of the platform user participating in the bidding is equal to its true cost and its utility is maximized.
The objective of the platform in (2) above is to maximize the utility of the platformThe V (S) is the platform benefit, the platform benefit and the credibility of the winning mark user are in a direct proportion relation with the continuous high-score factor, the user with higher credibility can bring better benefit to the platform, and the platform user with higher continuous high-score factor has stability, so that each stable benefit can be brought to the platform.
In the above (2), the utility is maximized when the bidding price of the platform user participating in the bidding is equal to the real cost thereof. The utility calculation formula of the user is as follows: wherein ,pi Rewards for winning users, ++>The user declares the cost of the perceived task for the winning bid.
The embodiment of the application provides a privacy protection incentive mechanism device based on reputation, which comprises the following components: a task acquisition module 201, a task publishing module 202, a bid information acquisition module 203, a reputation information acquisition module 204, a winning bid user determination module 205, a reward payment module 206, a perception data acquisition module 207, and a data update module 208.
The task obtaining module 201 is configured to obtain a perceived task uploaded by a task requester.
The task publishing module 202 is configured to publish the perceived task to the platform user.
The bid information acquisition module 203 is configured to acquire bid information, where the bid information includes a bid generated by the platform user when receiving a perception task.
Reputation information acquisition module 204 further comprises a determination module. Reputation information acquisition module 204 is used to acquire reputation information for each bidding platform user, the reputation information comprising a reputation value and a continuous high score factor. The judging module is used for obtaining the credit information of each bidding platform user, and comprises the following steps: judging whether each bidding platform user stores a credit value and a continuous high score factor in the platform; if the judgment result is negative, the platform user is indicated to be a new user, and an initial credit value and an initial continuous high score factor are distributed to the platform user; if the judgment result is yes, the platform user is the old user, and the corresponding reputation value and continuous high score factor on the platform are obtained.
The winning bid user determination module 205 further includes a winning bid probability calculation module. Each platform user may accomplish multiple perception tasks, and each perception task may be accomplished by only one platform user. The winning bid user determination module 205 is configured to determine winning bid users based on the bid information and the reputation information, and includes: calculating the probability of each bidding platform user being selected according to the reputation information and the bidding information of each bidding platform user; randomly selecting one of the quotations as a winning bid of the task according to the probability distribution, and adding the winning bid into a winning bid quotation set; a flat user having at least one bid belonging to the winning bid set of bids is determined as a winning bid user. The winning bid probability calculation module is used for distributing the probability of each bidding platform user to be selected according to the credit information and the bidding information of each bidding platform user, and the calculation formula is as follows:
wherein ,bi Representing the bid of the ith bidding platform user; r is (r) i Representing a reputation value of an ith bid for the bidding platform user; η (eta) i A continuous high score factor representing the ith bidding platform user; q i Bid level indicating that the bid is greater than zeroA station user.
The reward payment module 206 also includes a reward payment module. The reward payment module 206 is configured to pay a user reward for winning the mark; the reward payment module is used for paying the rewards of winning users as follows:
wherein ,bi Representing the bid of the ith bidding platform user; pr (Pr) i Representing a probability that the ith bidding platform user is selected; b max Representing the maximum of the bid amounts of the users of the bidding platform.
The sensing data acquisition module 207 is configured to acquire sensing data of the winning logo user and send the sensing data to the task requester.
The data update module 208 also includes a reputation calculation module. The data updating module 208 is configured to obtain the score of the task requester on the winning target user, and update the reputation information of the winning target user according to the score. The calculation formula of the reputation calculation module for the reputation value is as follows:
wherein ,indicating the total number of times the winning bid user bid is selected, +.>Threshold representing reputation value, ++>Representing the first round of scores of users of a bidding platform, beta l A time decay factor representing the first round based on the Ebinhaos 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 a high score; when the user score of the platform is larger than a preset value, the user score is recorded as a primary high score; f (t) represents a Pasi growth curve function.
Among the above modules, the platform firstly acquires the perceived task uploaded by the task requester through the task acquisition module 201, then distributes the perceived task to the platform through the task distribution module 202, the platform user acquires the perceived task distributed on the platform through the bid information acquisition module 203, bids the task interested in the task to generate quotation, the platform acquires the platform user interested in the perceived task, and acquires the reputation information of the platform user who has bid through the reputation information acquisition module 204, the reputation information comprises a reputation value and a continuous high score factor, the winning user is determined through the winning user determination module 205 for the reputation information and the bidding information, the reward corresponding to the winning user is calculated through the reward payment module 206, and the platform simultaneously acquires the perceived data of the winning user through the perception data acquisition module 207 and transmits the perceived data of the winning user to the task requester, and the reputation value information of the winning user is updated according to the score.
Although the application provides method operational steps as described in the examples or flowcharts, more or fewer operational steps may be included based on conventional or non-inventive labor. The order of steps recited in the present embodiment is only one way of performing the steps in a plurality of steps, and does not represent a unique order of execution. When implemented by an actual device or client product, the method of the present embodiment or the accompanying drawings may be performed sequentially or in parallel (e.g., in a parallel processor or a multithreaded environment).
The apparatus or module set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. For convenience of description, the above devices are described as being functionally divided into various modules, respectively. The functions of the various modules may be implemented in the same piece or pieces of software and/or hardware when implementing the present application. Of course, a module that implements a certain function may be implemented by a plurality of sub-modules or a combination of sub-units.
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;
memory 301 is used to store computer executable instructions;
processor 302 is configured to execute computer-executable instructions to implement a method for reputation-based privacy-preserving incentive mechanisms.
The storage medium includes, but is not limited to, a random access Memory (English: random Access Memory; RAM), a Read-Only Memory (ROM), a Cache Memory (English: cache), a Hard Disk (English: hard Disk Drive; HDD), or a Memory Card (English: memory Card). 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 a method for realizing privacy protection incentive mechanism based on reputation when executing the executable instructions.
The methods, apparatus or modules described in this application may be implemented in computer readable program code means and the controller may be implemented in any suitable way, for example, the controller may take the form of 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 (english: application Specific Integrated Circuit; abbreviated: ASIC), programmable logic controller and embedded microcontroller, examples of the controller including but 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 of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller can be regarded as a hardware component, and means for implementing various functions included therein can also be regarded as a structure within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
Some of the modules of the apparatus of the present application 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 embodiments, it will be apparent to those skilled in the art that the present application may be implemented in software plus necessary hardware. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product or may be embodied in the implementation of data migration. The computer software product may be stored on a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., comprising 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 application.
In this specification, each embodiment is described in a progressive manner, and the same or similar parts of each embodiment are referred to each other, and each embodiment is mainly described as a difference from other embodiments. All or portions of the present application are operational with numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet 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 for illustrating the technical solution of the present application, and not for limiting the present application; although the 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 scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced with equivalents; such modifications and substitutions do not depart from the spirit of the application.
Claims (7)
1. A reputation-based privacy-preserving incentive mechanism method comprising:
acquiring a perception task uploaded by a task requester;
issuing the perception task to a platform user;
acquiring bidding information, wherein the bidding information comprises quotations generated by the platform user when the platform user receives the perception task;
the method comprises the steps of obtaining reputation information of each bidding platform user, wherein the reputation information comprises a reputation value and a continuous high score factor;
determining winning bid users according to the bid information and the reputation information;
paying the winning logo user reward;
obtaining the perception data of the winning logo user and sending the perception data to the task requester;
obtaining the score of the task requester on the winning target user, and updating the credit information of the winning target user according to the score;
the platform memorizes and calculates the selected probability of each platform user according to the differential privacy index, and selects according to the probabilityThe specific process of selecting the winning target user based on the differential privacy index mechanism comprises the following steps: providing a differential privacy algorithm M, wherein O is all possible output sets of the differential privacy algorithm M, and for any subset O of any adjacent data sets A and B and O, pr [ M (A) ∈O is provided]≤exp(ε)×Pr[M(B)∈O]In order to realize differential privacy, the platform gives an availability function q (A, o) based on an exponential mechanism, a partial function on a return result can be obtained according to q (A, o), the return result is randomly output according to the partial function, the probability Pr of winning targets selected by the platform user is set to meet the differential privacy of the exponential mechanism, q for the availability function in the exponential mechanism, Δq is the global sensitivity of the availability function, and the probability of winning the target for the perceived task by the platform user satisfies the following conditions:
the determining winning bid user according to the bid information and the reputation information comprises the following steps:
calculating the probability of each bidding platform user being selected according to the reputation information and the bidding information of each bidding platform user, wherein the calculation formula is as follows:
wherein ,bi Representing an ith bid for the platform user; r is (r) i Representing a reputation value for an ith bid that has been bidding on the platform user; η (eta) i Representing the ith successive high scoring factors that have been bid on the platform user;
randomly selecting one of the quotations as a winning bid of the task according to the probability distribution, and adding the winning bid into a winning bid quotation set;
determining said flat user having at least one bid belonging to said winning bid set of bids as said winning bid user;
and when the credit information of the winning logo user is updated according to the score, the credit value is calculated according to the following formula:
wherein ,indicating the total number of times the winning bid user bid is selected, +.>Threshold value representing the reputation value, +.>Indicating that the platform user has been bidding for the first round of scores, beta l A time decay factor representing the first round based on the Ebinhaos 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 a high score; when the user score of the platform is larger than a preset value, marking as one-time high score; f (t) represents a Pasi growth curve function.
2. The method of claim 1, wherein the obtaining reputation information for each of the bidding platform users comprises:
judging whether each bidding 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 indicated to be a new user, and an initial reputation value and an initial continuous high score factor are distributed to the platform user;
and if the judgment result is yes, the platform user is an old user, and the corresponding reputation value and the continuous high-score factor on the platform are obtained.
3. The method of claim 1 wherein the consideration paid to the winning subject user is as follows:
wherein ,bi Representing an ith bid for the platform user; pr (Pr) i Representing the probability that the ith bid was made for the platform user to be selected; b max Representing the maximum of the offers that have been bid on the platform user.
4. A method according to any of claims 1-3, wherein each of said platform users is capable of performing a plurality of said perceived tasks, and each of said perceived tasks is capable of being performed by only one of said platform users.
5. A reputation-based privacy-preserving incentive mechanism comprising:
the task acquisition module is used for acquiring a perceived task uploaded by a task requester;
the task issuing module is used for issuing the perceived task to a platform user;
the bid information acquisition module is used for acquiring bid information, wherein the bid information comprises quotations generated by the platform user when the platform user receives the perception task;
the reputation information acquisition module is used for acquiring reputation information of each bidding platform user, wherein the reputation information comprises a reputation value and a continuous high-score factor;
and a winning bid user determining module for determining winning bid users according to the bidding information and the reputation information, comprising: according to the reputation information and the bidding information of each bidding platform user, the probability that each bidding platform user is selected is calculated, and the calculation formula is as follows:
wherein the bid is representative of the ith bidding platform user; representing a reputation value of an ith bid for the bidding platform user; a continuous high score factor representing the ith bidding platform user; representing a bidding platform user with a bid greater than zero; randomly selecting one of the quotations as a winning bid of the task according to the probability distribution, and adding the winning bid into a winning bid quotation set; determining a flat user having at least one bid belonging to the winning bid set of bids as a winning bid user;
a reward payment module for paying the user reward of the winning mark;
the perception data acquisition module is used for acquiring the perception data of the winning logo user and sending the perception data to the task requester;
the data updating module is used for acquiring the scores of the task requesters on the winning target users and updating the credit information of the winning target users according to the scores; the data updating module further comprises a reputation calculating module, wherein the reputation calculating module is used for calculating a reputation value according to the following formula:
wherein ,indicating the total number of times the winning bid user bid is selected, +.>Threshold value representing the reputation value, +.>Indicating that the platform user has been bidding for the first round of scores, beta l A time decay factor representing the first round based on the Ebinhaos forgetting curve;
the calculation formula of the continuous high-score factor is as follows: η= 1+f (t);
wherein t represents the platformThe number of times the user continuously obtains the high score; when the user score of the platform is larger than a preset value, marking as one-time high score; f (t) represents a Pasi growth curve function; the platform memorizes and calculates the selected probability of each platform user according to the differential privacy index, and selects winning users according to the probability, and the specific process of selecting winning users based on the differential privacy index mechanism is as follows: providing a differential privacy algorithm M, wherein O is all possible output sets of the differential privacy algorithm M, and for any subset O of any adjacent data sets A and B and O, pr [ M (A) ∈O is provided]≤exp(ε)×Pr[M(B)∈O]In order to realize differential privacy, the platform gives an availability function q (A, o) based on an exponential mechanism, a partial function on a return result can be obtained according to q (A, o), the return result is randomly output according to the partial function, the probability Pr of winning targets selected by the platform user is set to meet the differential privacy of the exponential mechanism, q is the availability function in the exponential mechanism, deltaq is the global sensitivity of the availability function, and the probability of winning targets of the perception tasks of the platform user meets the following conditions:
6. a server for a reputation-based privacy-preserving incentive mechanism, comprising a memory and a processor;
the memory is used for storing computer executable instructions;
the processor is configured to execute the computer-executable instructions to implement the method of any of claims 1-4.
7. A computer readable storage medium storing executable instructions which when executed by a computer enable the method of any one of claims 1 to 4.
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