Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (3): 202-208.doi: 10.11896/j.issn.1002-137X.2019.03.030

• Information Security • Previous Articles     Next Articles

Double-auction-based Incentive Mechanism for k-anonymity

TONG Hai1,2,BAI Guang-wei1,SHEN Hang1,3   

  1. (College of Computer Science and Technology,Nanjing Tech University,Nanjing 211816,China)1
    (State Key Laboratory for Novel Software Technology (Nanjing University),Nanjing 210093,China)2
    (National Engineering Research Center for Communication and Network Technology (Nanjing University of Posts and Telecommunications),Nanjing 210003,China)3
  • Received:2018-02-11 Revised:2018-05-14 Online:2019-03-15 Published:2019-03-22

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Abstract: kk-anonymity has become one of the most important location privacy technologies in LBS (Location Based Service).At least k users should be required to build an anonymous set,in which any user cannot be distinguished from other k－1 users.However,many users are not interested in their location privacy,so they have little interest in participating in the construction of anonymous sets.In order to improve the enthusiasm of users to participate in building anonymous sets,this paper proposed a double-auction-based incentive(DAI) mechanism for k-anonymity,which maximizes both the utility of buyers and sellers while guaranteeing fair transaction.To this end,multi-stage sample is used to filter the candidate user sets,then a reasonable remuneration and the winning set of users are determined according to budget balance.Finally,the rationality of the mechanism is provided in consideration of individual rationality,computation efficiency,budget balance and truthfulness,and so on.Simulation results demonstrate that DAI can solve the problem of malicious competition in the existing methods,and improve satisfaction and utility of buyers effectively.

Key words: k-anonymity, Double auction, Incentive mechanism, Location privacy

CLC Number: 

  • TP393
[1]SWEENEY L.k-anonymity:A model for protecting privacy[J].International Journal of Uncertainty,Fuzziness and Knowledge-Based Systems,2002,10(5):557-570.
[2]YANG D,XUE G,FANG X,et al.Crowdsourcing to smartphones:incentive mechanism design for mobile phone sensing[C]∥International Conference on Mobile Computing and Networking.ACM,2012:173-184.
[3]YANG D,FANG X,XUE G.Truthful incentive mechanisms for k-anonymity location privacy[C]∥IEEE INFOCOM.IEEE,2013:2994-3002.
[4]YUAN Z,WEI T,SHENG Z.On Designing Satisfaction-Ratio-Aware Truthful Incentive Mechanisms for k-Anonymity Location Privacy[J].IEEE Transactions on Information Forensics & Security,2016,11(11):2528-2541.
[5]WU Y,PENG H,PENG H,et al.MagiCrowd:A crowd based incentive for location-aware crowd sensing[C]∥Wireless Communications and NETWORKING Conference.IEEE,2016.
[6]WANG X,LIU Z,TIAN X,et al.Incentivizing Crowdsensing with Location-Privacy Preserving[J].IEEE Transactions on Wireless Communications,2017,PP(99):1.
[7]SHI W S,SUN H,CAO J,et al.Edge Computing:An Emerging Computing Model for Internet of Everything Era[J].Journal of Computing Research and Development,2017,54(5):907-924.(in Chinese)
施巍松,孙辉,曹杰,等.边缘计算:万物互联时代新型计算模型[J].计算机研究与发展,2017,54(5):907-924.
[8]KAWAJIRI R,SHIMOSAKA M,KASHIMA H.Steered crowd-
sensing:incentive design towards quality-oriented place-centric crowdsensing[C]∥ACM International Joint Conference on Pervasive and Ubiquitous Computing.ACM,2014:691-701.
[9]LI X,MIAO M,LIU H,et al.An incentive mechanism for K -anonymity in LBS privacy protection based on credit mechanism[J].Soft Computing,2017,21(14):3907-3917.
[10]REDDY S,ESTRIN D,HANSEN M,et al.Examining micro-payments for participatory sensing data collections∥Proceedings of the 12th ACM International Conference on Ubiquitous Computing.ACM,2010:33-36.
[11]DANEZIS G,LEWIS S,ANDERSON R.How much is location privacy worth[C]∥Proceedings of the Workshop on the Economics of Information Security Series (WEIS).ACM,2005.
[12] LEE J S,HOH B.Sell your experiences:a market mechanism based incentive for participatory sensing[C]∥IEEE InternationalConference on Pervasive Computing and Communications.IEEE,2010:60-68.
[13] LUO T,TAN H P,XIA L.Profit-maximizing incentive for participatory sensing[C]∥ Proceedings－IEEE INFOCOM.IEEE,2014:127-135.
[14]FENG Z,ZHU Y,ZHANG Q,et al.TRAC:Truthful auction for location-aware collaborative sensing in mobile crowdsourcing[C]∥Proceedings－IEEE INFOCOM.IEEE〈2014:1231-1239.
[15]ZHANG H,LIU B,SUSANTO H,et al.Incentive mechanism for proximity-based Mobile Crowd Service systems[C]∥IEEE INFOCOM 2016-IEEE International Conference on Computer Communications.IEEE,2016:1-9.
[16]ZHANG X,YANG Z,ZHOU Z,et al.Free Market of Crowd-
sourcing:Incentive Mechanism Design for Mobile Sensing[J].IEEE Transactions on Parallel & Distributed Systems,2014,25(12):3190-3200.
[17]ZHAO D,LI X Y,MA H.Budget-Feasible Online Incentive
Mechanisms for Crowdsourcing Tasks Truthfully[J].IEEE/ACM Transactions on Networking,2016,24(2):647-661.
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