计算机科学 ›› 2023, Vol. 50 ›› Issue (1): 294-301.doi: 10.11896/jsjkx.220400101
徐苗苗, 陈珍萍
XU Miaomiao, CHEN Zhenping
摘要： 针对连续群智感知中隐私要求提高、收集到的感知数据不可靠和用户参与感知任务积极性低等问题,提出了一种基于对称加密和双层真值发现的连续群智感知激励机制(Symmetric Encryption and Double Truth Discovery Based Incentive Mechanism,SDIM)。首先,使用对称加密算法对感知数据进行隐私保护,在隐私要求较高并且感知数据量较大时,可以降低计算开销,减少数据加密和奖励计算的时间。其次,基于双层真值发现模型提出了一种支持数据可靠性评估的激励机制,实现连续群智感知的实时奖励,并在参与者有恶意行为时提高奖励公平性。最后给出了SDIM的双重隐私性分析。仿真结果表明,SDIM可以根据数据可靠性有效地计算出真值和奖励,在数据加密和奖励分发的时间上明显优于对比模型,并在参与者有恶意行为时能够更加公平地计算奖励。
中图分类号:
| [1]CAO L,CAI Y,YUE Y G.Swarm Intelligence-Based Perfor-mance Optimization for Mobile Wireless Sensor Networks:Survey,Challenges,and Future Directions [J].IEEE Access,2019,7:161524-161553. [2]ZHOU Y K,RAO B,WANG W.UAV Swarm Intelligence:Recent Advances and Future Trends [J].IEEE Access,2020,8:183856-183878. [3]ZHANG Y H,DENG R H,ZHENG D,et al.Efficient and Robust Certificateless Signature for Data Crowdsensing in Cloud-Assisted Industrial IoT [J].IEEE Transactions on Industrial Informatics,2019,15(9):5099-5108. [4]HUANG H Y,CHEN D,LI Y T.IM-LDP:Incentive Mecha-nism for Mobile Crowd-Sensing Based on Local Differential Privacy[J].IEEE Communications Letters,2021,25(3):960-964. [5]ZHAO B W,TANG S H,LIU X M,et al.PACE:Privacy-Preserving and Quality-Aware Incentive Mechanism for Mobile Crowdsensing [J].IEEE Transactions on Mobile Computing,2021,20(5):1924-1939. [6]ZHAO C,YANG S S,MCCANN J A.On the Data Quality inPrivacy-Preserving Mobile Crowdsensing Systems with Untruthful Reporting [J].IEEE Transactions on Mobile Computing,2021,20(2):647-661. [7]ZHANG Y F,ZHANG X L.Price Learning-based IncentiveMechanism for Mobile Crowd Sensing[J].ACM Transactions on Sensor Networks,2021,17(2):1-24. [8]DAN P,FAN W,CHEN G H.Data Quality Guided IncentiveMechanism Design for Crowdsensing [J].IEEE Transactions on Mobile Computing,2018,17(2):307-319. [9]LIU Y X,TANG S H,WU H T,et al.RTPT:A Framework for Real-Time Privacy-Preserving Truth Discovery on Crowd Sensed Data Streams[J].Computer Networks,2019,148(15):349-360. [10]HE D J,CHAN S,GUIZANI M.User Privacy and Data Trustworthiness in Mobile Crowd Sensing [J].IEEE Wireless Communications,2015,22(1):28-34. [11]ZHAO B W,LIU X M,CHEN W N,et al.PRICE:Privacy and Reliability-Aware Real-Time Incentive System for Crowdsensing [J].IEEE Internet of Things Journal,2021,8(24):17584- 17595. [12]XUE K P,ZHU B,YANG Q Y,et al.InPPTD:A Lightweight Incentive-Based Privacy-Preserving Truth Discovery for Crowd-sensing Systems [J].IEEE Internet of Things Journal,2021,8(6):4305-4316. [13]SHI F,WU D,ARKHIPOV D I,et al.ParkCrowd:ReliableCrowdsensing for Aggregation and Dissemination of Parking Space Information [J].IEEE Transactions on Intelligent Transportation Systems,2019,20(11):4032-4044. [14]XU C,WANG J C,ZHU L H,et al.PPMR:A Privacy-Preserving Online Medical Service Recommendation Scheme in eHealthcare System [J].IEEE Internet of Things Journal,2019,6(3):5665-5673. [15]CAPPONI A,FIANDRINO C,KANTARCI B,et al.A Survey on Mobile Crowdsensing Systems:Challenges,Solutions,and Opportunities [J].IEEE Communications Surveys & Tutorials,2019,21(3):2419-2465. [16]CHEN X,TANG C,LI Z,et al.A Pricing Approach Toward Incentive Mechanisms for Participant Mobile Crowdsensing in Edge Computing [J].Mobile Networks and Applications,2020,25(3):1220-1232. [17]WANG J Z,LI M R,HE Y H,et al.A Blockchain Based Privacy-Preserving Incentive Mechanism in Crowdsensing Applications [J].IEEE Access,2018,6:17545-17556. [18]WANG D,REN J,WANG Z,et al.Privacy-preserving Streaming Truth Discovery in Crowdsourcing with Differential Privacy[J].IEEE Transactions on Mobile Computing,2022,21(10):3757-3772. [19]LI Q H,CAO G H,PORTA T F L.Efficient and Privacy-Aware Data Aggregation in Mobile Sensing[J].IEEE Transactions on Dependable and Secure Computing,2014,11(2):115-129. [20]LI Q,LI Y,GAO J,et al.Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation[C]//Proceedings of ACM International Conference on Management of Data.2014:1187-1198. [21]TANG J,FU S,XU M,et al.Achieve Privacy Preserving Truth Discovery in Crowdsensing Systems[C]//Proceedings of ACM International Conference on Information and Knowledge Mana-gement.2019:1301-1310. |
| [1] | 陈得鹏, 刘肖, 崔杰, 何道敬. 面向机器学习的成员推理攻击综述 Survey of Membership Inference Attacks for Machine Learning 计算机科学, 2023, 50(1): 302-317. https://doi.org/10.11896/jsjkx.220800227 |
| [2] | 鲁晨阳, 邓苏, 马武彬, 吴亚辉, 周浩浩. 基于分层抽样优化的面向异构客户端的联邦学习 Federated Learning Based on Stratified Sampling Optimization for Heterogeneous Clients 计算机科学, 2022, 49(9): 183-193. https://doi.org/10.11896/jsjkx.220500263 |
| [3] | 汤凌韬, 王迪, 张鲁飞, 刘盛云. 基于安全多方计算和差分隐私的联邦学习方案 Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy 计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108 |
| [4] | 吕由, 吴文渊. 隐私保护线性回归方案与应用 Privacy-preserving Linear Regression Scheme and Its Application 计算机科学, 2022, 49(9): 318-325. https://doi.org/10.11896/jsjkx.220300190 |
| [5] | 王健. 基于隐私保护的反向传播神经网络学习算法 Back-propagation Neural Network Learning Algorithm Based on Privacy Preserving 计算机科学, 2022, 49(6A): 575-580. https://doi.org/10.11896/jsjkx.211100155 |
| [6] | 李利, 何欣, 韩志杰. 群智感知的隐私保护研究综述 Review of Privacy-preserving Mechanisms in Crowdsensing 计算机科学, 2022, 49(5): 303-310. https://doi.org/10.11896/jsjkx.210400077 |
| [7] | 王美珊, 姚兰, 高福祥, 徐军灿. 面向医疗集值数据的差分隐私保护技术研究 Study on Differential Privacy Protection for Medical Set-Valued Data 计算机科学, 2022, 49(4): 362-368. https://doi.org/10.11896/jsjkx.210300032 |
| [8] | 吕由, 吴文渊. 基于同态加密的线性系统求解方案 Linear System Solving Scheme Based on Homomorphic Encryption 计算机科学, 2022, 49(3): 338-345. https://doi.org/10.11896/jsjkx.201200124 |
| [9] | 孔钰婷, 谭富祥, 赵鑫, 张正航, 白璐, 钱育蓉. 基于差分隐私的K-means算法优化研究综述 Review of K-means Algorithm Optimization Based on Differential Privacy 计算机科学, 2022, 49(2): 162-173. https://doi.org/10.11896/jsjkx.201200008 |
| [10] | 杨鸿健, 胡学先, 李可佳, 徐阳, 魏江宏. 隐私保护的非线性联邦支持向量机研究 Study on Privacy-preserving Nonlinear Federated Support Vector Machines 计算机科学, 2022, 49(12): 22-32. https://doi.org/10.11896/jsjkx.220500240 |
| [11] | 瞿祥谋, 吴映波, 蒋晓玲. 一种非独立同分布问题下的联邦数据增强算法 Federated Data Augmentation Algorithm for Non-independent and Identical Distributed Data 计算机科学, 2022, 49(12): 33-39. https://doi.org/10.11896/jsjkx.220300031 |
| [12] | 郭艳卿, 李宇航, 王湾湾, 付海燕, 吴铭侃, 李祎. 基于联邦学习的Gamma回归算法 FL-GRM:Gamma Regression Algorithm Based on Federated Learning 计算机科学, 2022, 49(12): 66-73. https://doi.org/10.11896/jsjkx.220600034 |
| [13] | 周倩, 戴华, 盛文杰, 胡正, 杨庚. 云环境下可验证关键词密文检索研究综述 Research on Verifiable Keyword Search over Encrypted Cloud Data:A Survey 计算机科学, 2022, 49(10): 272-278. https://doi.org/10.11896/jsjkx.220500285 |
| [14] | 李一聪, 周宽久, 王梓仲, 徐琳. ZKFERP:计算成本恒定的通用高效范围证明方案 ZKFERP:Universal and Efficient Range Proof Scheme with Constant Computational Cost 计算机科学, 2022, 49(10): 335-343. https://doi.org/10.11896/jsjkx.210900044 |
| [15] | 金华, 朱靖宇, 王昌达. 视频隐私保护技术综述 Review on Video Privacy Protection 计算机科学, 2022, 49(1): 306-313. https://doi.org/10.11896/jsjkx.201200047 |
|
||
首页