Crowdsourcing Privacy Protection Method to Local Differential Privacy

Abstract

Abstract: With the development of mobile Internet, significant progress has been made in mobile Internet based on location service (LBS) technology. Aiming at the problem of the risk of leakage of data and information privacy when individual users perform precise positioning, this paper proposes a Geo-indistinguishable disturbance method based on localized differential privacy. Before the user’s real location data information flows out of the client, a Geo-indistinguishable location disturbance method is used to act on the real location to obtain approximate location data. After the server receives it, the secondary area grid map is made, and then differential privacy worker count of the graph is disturbed, and finally the experiment is verified under the spatial range query, and compared with the satisfaction-localized differential privacy disturbance algorithm, the accuracy is increased by 2.7%, and the experiment is compared with the average division of the privacy budget allocation method. Area counting accuracy is improved by 4.57%。

Key words: local differential privacy, Geo-indistinguishable, location data, secondary area grid

ZHAO Long, LONG Shi-gong, . Crowdsourcing Privacy Protection Method to Local Differential Privacy[J]. Computer and Modernization, 2021, 0(07): 115-119.

References

［1］霍峥,孟小峰. 轨迹隐私保护技术研究［J］. 计算机学报, 2011(10):1820-1830.
［2］熊平,朱天清,王晓峰. 差分隐私保护及其应用［J］. 计算机学报, 2014,37(1):101-122.
［3］王璐,孟小峰. 位置大数据隐私保护研究综述［J］. 软件学报, 2014,25(4):693-712.
［4］ HUANG Y, HUO Z, MENG X F. CoPrivacy: A collaborative location privacy-preserving method without cloaking region［J］. Chinese Journal of Computers, 2011,34(10):1976-1985.

［5］ SEI Y, OHSUGA A. An algorithm for privacy-preserving location data collection by probabilistic dummy generation［J］. IEEJ Transactions on Electronics Information and Systems, 2015,135(6):660-670.

［6］熊平,朱天清,王晓峰. 差分隐私保护及其应用［J］. 计算机学报, 2014,37(1):101-122.
［7］ CHATZIKOKOLAKIS K, PALAMIDESSI C, STRONATI M. Location privacy via geo-indistinguishability［J］. ACM SIGLOG News, 2015,2(3):46-69.
［8］刘彦. 位置大数据服务中面向信息分析的隐私保护方法研究［D］. 南京:南京邮电大学, 2017.
［9］叶阿勇,李亚成,马建峰,等. 基于服务相似性的k-匿名位置隐私保护方法［J］. 通信学报, 2014,35(11):162-169.
［10］霍峥,张坤,贺萍,等. 满足本地化差分隐私的众包位置数据采集［J］. 计算机应用, 2019,39(3):763-768.
［11］HUA J Y, TONG W, XU F Y, et al. A geo-indistinguishable location perturbation mechanism for location-based services supporting frequent queries［J］. IEEE Transactions on Information Forensics and Security, 2018,13(5):1155-1168.
［12］张琳,刘彦,王汝传. 位置大数据服务中基于差分隐私的数据发布技术［J］. 通信学报, 2016,37(9):46-54.
［13］ANDRS M E, BORDENABE N E, CHATZIKOKOLAKIS K, etal.Geo-indistinguishability: Differentialprivacyforlocation-based systems［C］// Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security. 2013:901-914.
［14］CHATZIKOKOLAKIS K, PALAMIDESSI C, STRONATI M. Geo-indistinguishability: A principled approach to location privacy［C］// International Conference on Distributed Computing and Internet Technology (ICDCIT’15). 2015:49-72.
［15］王婷婷,龙士工,丁红发. 大型社交网络的差分隐私保护算法［J］. 计算机工程与设计, 2020,41(6):1568-1574.
［16］陈婷婷,龙士工. 基于差分隐私的频繁项集挖掘的启发式算法［J］. 计算机工程与设计, 2019,40(2):412-417.
［17］唐正莉,龙士工. 基于混合机制下的差分隐私直方图发布［J］. 贵州大学学报(自然科学版), 2018(4):32-36.
［18］张浩铭,刘田天,龙士工. 优化结构下的差分隐私直方图发布［J］. 计算机仿真, 2019,36(3):226-230.
［19］ZHANG J D, GHINITA G, CHOW C Y. Differentially private location recommendations in geosocial networks［C］// 2014 IEEE 15th International Conference on Mobile Data Management. 2014:59-68.
［20］叶青青,孟小峰,朱敏杰,等. 本地化差分隐私研究综述［J］. 软件学报, 2018,29(7):159-183.
［21］罗惠雯,龙士工. 基于用户感兴趣区域的地理不可区分性的位置扰动算法［J］. 计算机应用, 2020,40(3):760-764.
［22］TO H, GHINITA G, SHAHABI C. A framework for protecting worker location privacy in spatial crowdsourcing［C］// Proceedings of the VLDB Endowment. 2014,7(10):919-930.
［23］NGUYN T, XIAO X K, YANG Y, et al. Collecting and analyzing data from smart device users with local differential privacy［J］. arXiv preprint arXiv:1606.05053, 2016.