Details of Research Outputs

TitleSocial-Aware Privacy-Preserving Mechanism for Correlated Data
Author (Name in English or Pinyin)
Guocheng Liao1; Xu Chen2; Jianwei Huang3,4
Date Issued2020-04-18
Source PublicationIEEE-ACM TRANSACTIONS ON NETWORKING
ISSN1063-6692
DOI10.1109/TNET.2020.2994213
Firstlevel Discipline计算机科学技术
Education discipline科技类
Published range国外学术期刊
References
[1] G. Liao, X. Chen, and J. Huang, "Social-aware privacy-preserving correlated data collection," in Proc. 18th ACM Int. Symp. Mobile Ad Hoc Netw. Comput., Jun. 2018, pp. 11-20.
[2] (Apr. 2017). Facebook-Cambridge Analytica Data Scandal. [Online]. Available: https://en.wikipedia.org/wiki/Facebook-Cambridge _Analytica_data_scandal
[3] F. Brunton and H. Nissenbaum, Obfuscation: A User's Guide for Privacy and Protest. Cambridge, MA, USA: MIT Press, 2015.
[4] E. Ayday, E. De Cristofaro, J.-P. Hubaux, and G. Tsudik, "The chills and thrills of whole genome sequencing," IEEE Comput. Mag., early access, Sep. 10, 2013, doi: 10.1109/MC.2013.333.
[5] F. Stajano, L. Bianchi, P. Liò, and D. Korff, "Forensic genomics: Kin privacy, driftnets and other open questions," in Proc. 7th ACM Workshop Privacy Electron. Soc. WPES, 2008, pp. 15-22.
[6] F. McSherry and K. Talwar, "Mechanism design via differential privacy," in Proc. 48th Annu. IEEE Symp. Found. Comput. Sci. (FOCS), Oct. 2007, pp. 94-103.
[7] L. K. Fleischer and Y.-H. Lyu, "Approximately optimal auctions for selling privacy when costs are correlated with data," in Proc. 13th ACM Conf. Electron. Commerce EC, 2012, pp. 568-585.
[8] A. Ghosh and A. Roth, "Selling privacy at auction," Games Econ. Behav., vol. 91, pp. 334-346, May 2015.
[9] J. Pawlick and Q. Zhu, "A mean-field stackelberg game approach for obfuscation adoption in empirical risk minimization," in Proc. IEEE Global Conf. Signal Inf. Process. (GlobalSIP), Nov. 2017, pp. 518-522.
[10] A. Ghosh, K. Ligett, A. Roth, and G. Schoenebeck, "Buying private data without verification," in Proc. 15th ACM Conf. Econ. Comput.-EC, 2014, pp. 931-948.
[11] J. Lin, D. Yang, M. Li, J. Xu, and G. Xue, "BidGuard: A framework for privacy-preserving crowdsensing incentive mechanisms," in Proc. IEEE Conf. Commun. Netw. Secur. (CNS), Oct. 2016, pp. 145-153.
[12] Z. Zhang, S. He, J. Chen, and J. Zhang, "REAP: An efficient incentive mechanism for reconciling aggregation accuracy and individual privacy in crowdsensing," IEEE Trans. Inf. Forensics Security, vol. 13, no. 12, pp. 2995-3007, Dec. 2018.
[13] H. Jin, L. Su, H. Xiao, and K. Nahrstedt, "INCEPTION: Incentivizing privacy-preserving data aggregation for mobile crowd sensing systems," in Proc. 17th ACM Int. Symp. Mobile Ad Hoc Netw. Comput.-MobiHoc, 2016, pp. 341-350.
[14] C. Dwork, F. McSherry, K. Nissim, and A. Smith, "Calibrating noise to sensitivity in private data analysis," in Proc. Theory Cryptography Conf., 2006, pp. 265-284.
[15] D. Kifer and A. Machanavajjhala, "No free lunch in data privacy," in Proc. Int. Conf. Manage. Data SIGMOD, 2011, pp. 193-204.
[16] D. Kifer and A. Machanavajjhala, "Pufferfish: A framework for mathematical privacy definitions," ACM Trans. Database Syst., vol. 39, no. 1, p. 3, 2014.
[17] X. He, A. Machanavajjhala, and B. Ding, "Blowfish privacy: Tuning privacy-utility trade-offs using policies," in Proc. ACM SIGMOD Int. Conf. Manage. Data SIGMOD, 2014, pp. 1447-1458.
[18] C. Liu, S. Chakraborty, and P. Mittal, "Dependence makes you vulnerable: Differential privacy under dependent tuples," in Proc. Netw. Distrib. Syst. Secur. Symp., 2016, pp. 21-24.
[19] T. Zhu, P. Xiong, G. Li, and W. Zhou, "Correlated differential privacy: Hiding information in non-IID data set," IEEE Trans. Inf. Forensics Security, vol. 10, no. 2, pp. 229-242, Feb. 2015.
[20] B. Yang, I. Sato, and H. Nakagawa, "Bayesian differential privacy on correlated data," in Proc. ACM SIGMOD Int. Conf. Manage. Data-SIGMOD, 2015, pp. 747-762.
[21] S. Song, Y. Wang, and K. Chaudhuri, "Pufferfish privacy mechanisms for correlated data," in Proc. ACM Int. Conf. Manage. Data-SIGMOD, 2017, pp. 1291-1306.
[22] R. Kindermann and J. L. Snell, Markov Random Fields and Their Applications, vol. 1. Providence, RI, USA: American Mathematical Society, 1980.
[23] A. Darwiche, Modeling and Reasoning With Bayesian Networks. Cambridge, U.K.: Cambridge Univ. Press, 2009.
[24] M. Chessa, J. Grossklags, and P. Loiseau, "A game-theoretic study on non-monetary incentives in data analytics projects with privacy implications," in Proc. IEEE 28th Comput. Secur. Found. Symp., Jul. 2015, pp. 90-104.
[25] W. Wang, L. Ying, and J. Zhang, "The value of privacy: Strategic data subjects, incentive mechanisms and fundamental limits," ACM SIGMETRICS Perform. Eval. Rev., vol. 44, no. 1, pp. 249-260, Jun. 2016.
[26] W. Wang, L. Ying, and J. Zhang, "A game-theoretic approach to quality control for collecting privacy-preserving data," in Proc. 53rd Annu. Allerton Conf. Commun., Control, Comput. (Allerton), Sep. 2015, pp. 474-479.
[27] W. Wang, L. Ying, and J. Zhang, "Buying data from privacy-aware individuals: The effect of negative payments," in Proc. Int. Conf. Web Internet Econ., 2016, pp. 87-101.
[28] X. Wu, T. Wu, M. Khan, Q. Ni, and W. Dou, "Game theory based correlated privacy preserving analysis in big data," IEEE Trans. Big Data, early access, May 5, 2017, doi: 10.1109/TBDATA.2017.2701817.
[29] Apple Inc. Differential Privacy Overview. Accessed: 2018. [Online]. Available: https://www.apple.com/privacy/docs/Differential_Privacy_ Overview.pdf
[30] Y. Chen and S. Zheng, "Prior-free data acquisition for accurate statistical estimation," in Proc. ACM Conf. Econ. Comput., Jun. 2019, pp. 659-677.
[31] H. Zhang, Y. Shu, P. Cheng, and J. Chen, "Privacy and performance trade-off in cyber-physical systems," IEEE Netw., vol. 30, no. 2, pp. 62-66, Mar. 2016.
[32] C. Dwork and A. Roth, "The algorithmic foundations of differential privacy," Found. Trends Theor. Comput. Sci., vol. 9, nos. 3-4, pp. 211-407, 2014.
[33] C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor, "Our data, ourselves: Privacy via distributed noise generation," in Proc. Annu. Int. Conf. Theory Appl. Cryptograph. Techn. Berlin, Germany: Springer, 2006.
[34] B. Jayaraman, L. Wang, D. Evans, and Q. Gu, "Distributed learning without distress: Privacy-preserving empirical risk minimization," in Proc. Adv. Neural Inf. Process. Syst., 2018, pp. 6343-6354.
[35] O. Goldreich, Foundations of Cryptography: Volume 2, Basic Applications. Cambridge, U.K.: Cambridge Univ. Press, 2009.
[36] V. Boẑović, D. Socek, R. Steinwandt, and V. I. Villányi, "Multi-authority attribute-based encryption with honest-but-curious central authority," Int. J. Comput. Math., vol. 89, no. 3, pp. 268-283, Feb. 2012.
[37] H. Jin, L. Su, and K. Nahrstedt, "Theseus: Incentivizing truth discovery in mobile crowd sensing systems," in Proc. 18th ACM Int. Symp. Mobile Ad Hoc Netw. Comput., Jul. 2017, pp. 1-10.
[38] P. Boyle and M. Frean, "Dependent Gaussian processes," in Proc. Adv. neural Inf. Process. Syst., 2005, pp. 217-224.
[39] C. E. Rasmussen, "Evaluation of Gaussian processes and other methods for non-linear regression," Ph.D. dissertation, Graduate Dept. Comput. Sci., Univ. Toronto, Toronto, ON, Canada, 1999.
[40] M. N. Gibbs, "Bayesian Gaussian processes for regression and classification," Ph.D. dissertation, Inferential Sci. Group Cavendish Lab., Univ. Cambridge, Cambridge, U.K., 1997.
[41] Z. Lin, M. Hewett, and R. B. Altman, "Using binning to maintain confidentiality of medical data," in Proc. AMIA Symp., 2002, p. 454.
[42] D. Agrawal and C. C. Aggarwal, "On the design and quantification of privacy preserving data mining algorithms," in Proc. 20th ACM SIGMOD-SIGACT-SIGART Symp. Princ. Database Syst. PODS, 2001, pp. 247-255.
[43] A. Ghosh, T. Roughgarden, and M. Sundararajan, "Universally utilitymaximizing privacy mechanisms," SIAM J. Comput., vol. 41, no. 6, pp. 1673-1693, Jan. 2012.
[44] X. Zhang, L. Gao, B. Cao, Z. Li, and M. Wang, "A double auction mechanism for mobile crowd sensing with data reuse," in Proc. GLOBECOM IEEE Global Commun. Conf., Dec. 2017, pp. 1-6.
[45] A. Mas-Colell et al., Microeconomic Theory, vol. 1. New York, NY, USA: Oxford Univ. Press, 1995.
[46] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2004.
[47] J. Leskovec and A. Krevl. (Jun. 2014). SNAP Datasets: Stanford Large Network Dataset Collection. [Online]. Available: http://snap.stanford. edu/data
[48] H. I. Bozma, "Computation of Nash equilibria: Admissibility of parallel gradient descent," J. Optim. Theory Appl., vol. 90, no. 1, pp. 45-61, Jul. 1996.
Citation statistics
Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Identifierhttps://irepository.cuhk.edu.cn/handle/3EPUXD0A/1395
CollectionSchool of Science and Engineering
Corresponding AuthorJianwei Huang
Affiliation
1.The Chinese University of Hong Kong
2.Sun Yat-sen Unversity
3.理工学院
4.Shenzhen Institute of Artificial Intelligence and Robotics for Society, the Chinese University of Hong Kong
Corresponding Author AffilicationSchool of Science and Engineering;  Shenzhen Institute of Artificial Intelligence and Robotics for Society
Recommended Citation
GB/T 7714
Guocheng Liao,Xu Chen,Jianwei Huang. Social-Aware Privacy-Preserving Mechanism for Correlated Data[J]. IEEE-ACM TRANSACTIONS ON NETWORKING,2020.
APA Guocheng Liao, Xu Chen, & Jianwei Huang. (2020). Social-Aware Privacy-Preserving Mechanism for Correlated Data. IEEE-ACM TRANSACTIONS ON NETWORKING.
MLA Guocheng Liao,et al."Social-Aware Privacy-Preserving Mechanism for Correlated Data".IEEE-ACM TRANSACTIONS ON NETWORKING (2020).
Files in This Item:
There are no files associated with this item.
Related Services
Usage statistics
Google Scholar
Similar articles in Google Scholar
[Guocheng Liao]'s Articles
[Xu Chen]'s Articles
[Jianwei Huang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Guocheng Liao]'s Articles
[Xu Chen]'s Articles
[Jianwei Huang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Guocheng Liao]'s Articles
[Xu Chen]'s Articles
[Jianwei Huang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.