CN115499186A - Privacy protection scheme based on secure multi-party calculation truth value discovery in mobile crowd sensing - Google Patents
Privacy protection scheme based on secure multi-party calculation truth value discovery in mobile crowd sensing Download PDFInfo
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Abstract
The invention relates to the technical field of Internet of things, and discloses a privacy finding method based on safe multi-party calculation truth in mobile crowd sensing, which relates to a privacy protection scheme. The privacy protection scheme is discovered based on the secure multi-party computing truth value in the mobile crowd sensing, a secret sharing technology and secure multi-party computing are adopted, a user only needs to upload data to a server, the server calculates the data truth value and the user weight by executing a secure multi-party computing protocol, interaction between the user and the server is reduced, and a cryptographic algorithm with high computing cost is not adopted, so that the scheme has high execution efficiency.
Description
Technical Field
The invention relates to the technical field of Internet of things, in particular to a privacy protection scheme based on safe multi-party calculation truth value discovery in mobile crowd sensing.
Background
With the continuous development and accelerated integration of the internet of things, big data and artificial intelligence technologies, mobile Crowd Sensing (MCS) is a new data sensing mode and becomes a research hotspot at the international leading edge in recent years. Unlike the traditional internet of things mode of fixedly deploying a sensor network, the MCS takes a large number of mobile intelligent terminals of common users as basic sensing units, performs data sensing on the physical world, acquires various information such as personal behaviors, equipment states and surrounding environments, and solves the problems of high system cost, difficult maintenance, limited scale, inflexible performance and the like in the traditional data collection mode. Today's portable intelligent terminal devices integrate a large number of sensor elements and have very strong computing performance, can meet the requirements of more and more sensing tasks on the device performance and have very low sensing cost. The low-cost and efficient data collection mode enables the MCS to be widely applied, such as health data collection, traffic condition analysis, environmental noise monitoring and the like, greatly enriches various applications and services of a novel smart city, and promotes realization of 'everything interconnection and smart intercommunication'. In mobile crowd-sourcing perception, mobile users are participants, both collectors and producers of data and users and beneficiaries. Various resources (such as power, flow, road charge and the like, and the consumed resources are collectively called as sensing cost) are inevitably consumed during the participation of the user in the sensing task, so that a reasonable incentive mechanism needs to be designed to ensure the enthusiasm of the user for participation. For example, in a monetary reward-based incentive mechanism, the sensory platform may offer additional monetary rewards to the user upon payment of the user's perceived cost, in order to incentivize continued participation by the user.
Some schemes require frequent user participation in the execution process, however, most users can hardly guarantee that the schemes are on line for a long time, which causes uncertainty in scheme execution, and although some researches solve the problem of interaction between users and servers to some extent, users only need to upload perception data and do not need to participate in any calculation, these schemes still inevitably use computationally expensive cryptography primitives to guarantee privacy security, which causes the calculation efficiency to decrease rapidly with the increase of perception users.
Disclosure of Invention
The invention aims to provide a privacy protection scheme based on safe multi-party calculation truth value discovery in mobile crowd sensing so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a privacy protection scheme is discovered based on a safe multi-party calculation truth value in mobile crowd sensing, and the privacy protection scheme comprises the following steps:
s1: the data requester specifies a desired perception task (e.g., ambient noise monitoring, indoor map information collection, and road congestion condition monitoring at a specified location, etc.) and requests a perception service from the perception platform.
S2: the sensory platform recruits sensory users.
S3: and the perception user collects perception data according to the use of the intelligent terminal equipment.
S4: the perception user uploads perception data to the perception platform, and in order to protect privacy, the perception user distributes the perception data to three servers of the perception platform by adopting a secret sharing technology.
S5：
The three servers in the perception platform adopt a secure multiparty arithmetic calculation protocol to execute the calculation of truth value discovery, and the calculated truth value is still the secret sharing share of the servers.
S6: the data requester can restore the true value after summarizing the true shared shares of the three servers.
Preferably, the collected perception data x is divided by the perception user
Respectively sent to three servers S 1 ,S 2 ,S 3 Storing and using
Presentation to a server S i Server S, share of 1 ,S 2 ,S 3 The privacy-preserving true-value discovery computation is performed jointly using a shared secure multi-party computation scheme.
Preferably, the shared secure multiparty computation scheme may implement basic computations, referred to herein as basic computation protocols, such as secure addition, multiplication, etc., e.g., given share of input data shares
And
server S i Can be calculated by performing
And
the addition and multiplication operations on the shared shares are realized without revealing any private information about the inputs and the computation output.
Preferably, the shared is based on additive secret sharing, so that addition and subtraction operations on shared shares are performed
And operation of multiplying a shared share by a constant
All can be directly realized according to the property of additive secret sharing, and the multiplication operation among shared shares needs to be constructed by using a Du-Atallah multiplication protocol.
Preferably, after the sensing user receives the sensing task, the S4 collects the sensing data by using the intelligent terminal device, and the sensing data is uploaded to the server by an additive secret sharing method.
Preferably, the perception platform recruits K perception users { u } 1 ,u 2 ,…,u K Help data requesters to collect M perceptual tasks T 1 ,T 2 ,…,T M The perception data of users u i All sensory data collected is noted as { x } i1 ,x i2 ,…,x iM In which x ij Representing user u i For task T j The perception data collected.
Preferably, the server S 1 ,S 2 ,S 3 Respectively hold calculated true share
Each server sends the true share held by each server to the data requester, and the data requester restores the true share according to the following formula
Preferably, the sensing task number M and the sensing user number K are public, and any one of the systems can be acquired at any time.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the privacy protection scheme discovered based on the secure multi-party computing truth value in the mobile crowd sensing, due to the fact that a secret sharing technology and secure multi-party computing are adopted, a user only needs to upload data to a server, the server calculates the data truth value and the user weight through executing a secure multi-party computing protocol, interaction between the user and the server is greatly reduced, and meanwhile, due to the fact that a cryptographic algorithm with high computing overhead is not adopted, the scheme has high execution efficiency.
2. In the mobile crowd sensing, a privacy protection scheme is discovered based on a safe multi-party computing true value, sensing data is split by adopting a secret sharing technology, and the true value is computed by adopting the safe multi-party computing among a plurality of servers at the cloud.
3. In the mobile crowd sensing, a privacy protection scheme is discovered based on a safe multiparty calculation truth value, a user incentive mechanism driven by data quality is designed by utilizing user weight obtained by truth value discovery calculation, and the protection of user privacy is realized.
Drawings
FIG. 1 is a diagram of a perceptual task system framework;
FIG. 2 is a diagram of a sensory platform system framework.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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.
Referring to fig. 1-2, the present invention provides the following technical solutions:
a privacy protection scheme is discovered based on a safe multi-party calculation truth value in mobile crowd sensing, and the privacy protection scheme comprises the following steps:
s1: a data requester specifies required sensing tasks (such as environmental noise monitoring at a specified position, indoor map information collection, road congestion condition monitoring and the like) and requests sensing services from a sensing platform, the number M of the sensing tasks and the number K of sensing users are public, and any one of the sensing tasks and the sensing users can be acquired at any time in the system.
S2: the sensory platform recruits sensory users.
S3: the perception user divides the perception data into perception data x according to the perception data x collected by using the intelligent terminal equipment
Respectively sent to three servers S 1 ,S 2 ,S 3 Storing and using
Presentation to the Server S i Share of, server S 1 ,S 2 ,S 3 The privacy-preserving true-value discovery computation is performed jointly using a shared secure multi-party computation scheme.
The shared secure multiparty computation scheme may implement basic computations, referred to herein as basic computation protocols, such as secure addition, multiplication, etc., e.g., given share of input data shares
And
server S i Can be calculated by performing
And
implementing addition and multiplication operations on shared shares without revealing any private information about inputs and computation outputs, shared being based on additive secret sharing, whereby addition and subtraction operations on shared shares are performed
And operation of multiplying a shared share by a constant
All the methods can be directly realized according to the property of additive secret sharing, and for the multiplication operation among shared shares, the multiplication operation needs to be constructed by using a Du-Atallah multiplication protocol, and the shared multiplication protocol is shown by an algorithm 2:
s4: the perception user uploads perception data to the perception platform, the perception user collects the perception data by using intelligent terminal equipment after receiving a perception task, the perception data are uploaded to the server through an additive secret sharing method, and in order to protect privacy, the perception user distributes the perception data to the three servers of the perception platform by adopting a secret sharing technology.
The perception platform recruits K perception users (u) 1 ,u 2 ,…,u K Help data requesters to collect M perceptual tasks T 1 ,T 2 ,…,T M The perception data of users u i All sensory data collected is noted as { x } i1 ,x i2 ,…,x iM In which x is ij Representing user u i For task T j Collected perception data, server S 1 ,S 2 ,S 3 Respectively hold calculated true share
Each server sends the true share held by each server to the data requester, and the data requester restores the true share according to the following formula
S5: the three servers in the perception platform adopt a secure multiparty arithmetic calculation protocol to execute the calculation of truth value discovery, and the calculated truth value is still the secret sharing share of the servers.
S6: the data requester can restore the true value after summarizing the true value sharing shares of the three servers, the perception user collects perception data by using intelligent terminal equipment after receiving the perception task, and the perception data are uploaded to the servers through an additive secret sharing method.
The perception platform recruits K perception users (u) 1 ,u 2 ,…,u K Help data requesters to collect M perceptual tasks T 1 ,T 2 ,…,T M The perception data of users u i All the perception data collectedIs denoted as { x i1 ,x i2 ,…,x iM In which x ij Representing user u i For task T j The perception data collected.
For perceptual data x ij Perception of user u i Generating random numbers
Calculating out
Namely obtaining X ij Is shared with
As follows
Finally, each perception user sends the calculated three sharing shares to the server S, the server S and the server S for storage.
The result obtained after the calculation of each server is the secret sharing share corresponding to the true value, according to the security of secret sharing, a single server cannot obtain the true result discovered by the true value, so that the security of privacy is ensured, after the calculation of the servers is finished, the respective true value sharing shares are sent to the data requester, the requester aggregates the received sharing shares to obtain the final true value result, the operations related to the true value discovery algorithm include addition, subtraction, multiplication, division and logarithm operation, the operation can be constructed by using a shared basic calculation protocol, the logarithm operation log (-) related in the weight updating step can be converted into ln (-) by adopting a bottom-changing formula, and then the approximation operation is carried out by using a Taylor series, and based on the given value, the logarithm log (x) can be represented as follows:
wherein
The logarithm algorithm is constructed according to the above formula as shown in algorithm 2:
both processes of true value discovery are essentially arithmetic operations, and the involved operations only include addition (subtraction), multiplication (division) and logarithm calculation, so that a secure multi-party arithmetic computation (secure-fractional arithmetic computing) protocol is adopted to realize true value computation, shared is a secure multi-party computing platform and provides secure multi-party arithmetic computation (such as addition, subtraction, multiplication and division and other basic operations) protocols, and all basic protocols are universal and combinable, even if a complex protocol constructed by basic operation protocols can still ensure privacy security, in the research, the true value discovery of privacy protection is realized based on the shared basic protocol, and the true value discovery of privacy protection based on the shared structure comprises the following steps:
server S i After receiving the true value data uploaded by the user, the slave computer
In the method, M random numbers (r) are randomly selected 1 ,r 2 ,…,r M ) As an initial truth share, i.e.
After the initial value setting is completed, the weight updating and the truth value updating are calculated in an iterative mode among the servers, and the calculation process is as follows:
updating the security weight: for the perception numberAccording to
True value discovery is performed among three servers by executing Sharmind secure multiparty computing protocol, where the server (k =1,2,3) calculates the share corresponding to the weight by executing the following formula
Updating a safety truth value: after the weight update calculation is completed, the three servers perform the calculation of the true value using the user weights calculated during the weight update process, wherein the server (k =1,2,3) calculates the corresponding share of the true value by performing the following equation
After performing the weight update and the truth update of multiple iterations, the server S 1 ,S 2 ,S 3 The execution formula calculates the user weight deviation (i.e. the difference between the user weight and the average weight)
And sending to a perception platform for summarizing:
aware platform execution
The user weight deviation is restored and the calculation of the distribution to each user is performed (12)User's award amount Pay i And the Budget paid by the data requester is the sensing Budget, and the K is a scaling coefficient for controlling the reward distribution:
the perception platform deploys three semi-trusted servers S 1 ,S 2 ,S 3 Compared with a double-server architecture, the three-server architecture adopted by the method can have higher tolerance on collusion behaviors, privacy can still be guaranteed not to be revealed even if collusion behaviors appear in any two servers, although more servers are deployed, higher collusion tolerance can be obtained, collusion attack is difficult to achieve, and in consideration of cost, efficiency and other factors, 3-5 servers are deployed to be a practical scheme.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in the embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. A privacy protection scheme is discovered based on a safe multi-party calculation truth value in mobile crowd sensing, and is characterized by comprising the following steps:
s1: a data requester specifies a required sensing task and requests a sensing service from a sensing platform;
s2: the perception platform recruits perception users;
s3: the perception user collects perception data according to the use of the intelligent terminal equipment;
s4: the perception user uploads perception data to the perception platform, and in order to protect privacy, the perception user distributes the perception data to three servers of the perception platform by adopting a secret sharing technology;
s5: the method comprises the following steps that three servers in a perception platform adopt a secure multiparty arithmetic calculation protocol to execute calculation of truth value discovery, and the calculated truth value is still a secret sharing share of the servers;
s6: the data requester can restore the true value after summarizing the true shared shares of the three servers.
2. The privacy protection scheme for secure multi-party computing based true value discovery in mobile crowd-sourcing awareness in accordance with claim 1, wherein: the perception data x collected by S3 is divided into the perception data x by perception users
Respectively sent to three servers S 1 ,S 2 ,S 3 Storing and using
Presentation to the Server S i Server S, share of 1 ,S 2 ,S 3 The privacy-preserving true-value discovery computation is performed jointly using a shared secure multi-party computation scheme.
3. The privacy protection scheme for secure multi-party computing based true value discovery in mobile crowd sensing according to claim 2, wherein: the shared secure multiparty computation scheme may implement basic computations, referred to herein as basic computation protocols, such as secure addition, multiplication, etc., e.g., given share of input data shares
And
server S i Can be calculated by performing
And
the addition and multiplication operations on the shared shares are realized without revealing any private information about the inputs and the computation output.
4. The privacy protection scheme for secure multi-party computing based true value discovery in mobile crowd-sourcing awareness in accordance with claim 3, wherein: the shared is based on additive secret sharing, so that addition and subtraction operations on shared shares are performed
And operation of multiplying a shared share by a constant
All can be directly realized according to the property of additive secret sharing, and the multiplication operation among shared shares needs to be constructed by using a Du-Atallah multiplication protocol.
5. The privacy protection scheme for secure multi-party computing based true value discovery in mobile crowd-sourcing awareness in accordance with claim 1, wherein: and S4, after receiving the perception task, the perception user collects perception data by using the intelligent terminal equipment, and the perception data is uploaded to the server by an additive secret sharing method.
6. The privacy protection scheme for secure multi-party computing based true value discovery in mobile crowd-sourcing awareness in accordance with claim 5, wherein: the perception platform recruits K perception users { u 1 ,u 2 ,…,u K Help data requesters to collect M perceptual tasks T 1 ,T 2 ,…,T M The perception data of users u i All sensory data collected is noted as { x } i1 ,x i2 ,…,x iM In which x ij Representing user u i For task T j The perception data collected.
7. The privacy protection scheme for mobile crowd sensing based on secure multi-party computation truth discovery according to claim 1, wherein: the server S 1 ,S 2 ,S 3 Respectively hold calculated true share
Each server sends the true share held by each server to the data requester, and the data requester restores the true share according to the following formula
8. The privacy protection scheme for secure multi-party computing based true value discovery in mobile crowd-sourcing awareness in accordance with claim 1, wherein: s1, the number M of sensing tasks and the number K of sensing users are public, and any party in the system can obtain the sensing tasks at any time.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108494760A (en) * | 2018-03-14 | 2018-09-04 | 安徽大学 | A kind of intelligent perception true value discovery method of protection privacy |
| US20200304293A1 (en) * | 2017-08-30 | 2020-09-24 | Inpher, Inc. | High-Precision Privacy-Preserving Real-Valued Function Evaluation |
| CN112104609A (en) * | 2020-08-20 | 2020-12-18 | 电子科技大学 | Method for verifiable privacy-aware true phase discovery in a mobile crowd-sourcing awareness system |
| CN112464174A (en) * | 2020-10-27 | 2021-03-09 | 华控清交信息科技(北京)有限公司 | Method and device for verifying multi-party secure computing software and device for verifying |
| CN112787809A (en) * | 2020-12-31 | 2021-05-11 | 华南理工大学 | Efficient crowd sensing data stream privacy protection truth value discovery method |
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- 2022-09-13 CN CN202211109100.9A patent/CN115499186A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200304293A1 (en) * | 2017-08-30 | 2020-09-24 | Inpher, Inc. | High-Precision Privacy-Preserving Real-Valued Function Evaluation |
| CN108494760A (en) * | 2018-03-14 | 2018-09-04 | 安徽大学 | A kind of intelligent perception true value discovery method of protection privacy |
| CN112104609A (en) * | 2020-08-20 | 2020-12-18 | 电子科技大学 | Method for verifiable privacy-aware true phase discovery in a mobile crowd-sourcing awareness system |
| CN112464174A (en) * | 2020-10-27 | 2021-03-09 | 华控清交信息科技(北京)有限公司 | Method and device for verifying multi-party secure computing software and device for verifying |
| CN112787809A (en) * | 2020-12-31 | 2021-05-11 | 华南理工大学 | Efficient crowd sensing data stream privacy protection truth value discovery method |
Non-Patent Citations (1)
| Title |
|---|
| 冯琦: "基于安全多方计算的数据隐私保护技术研究", 中国博士学位论文全文数据库 信息科技辑, 15 August 2022 (2022-08-15), pages 1 * |
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