article

Customized privacy preserving for inherent data and latent data

Authors:

Xiuzhen ChengAuthors Info & Claims

Personal and Ubiquitous Computing, Volume 21, Issue 1

Pages 43 - 54

https://doi.org/10.1007/s00779-016-0972-2

Published: 01 February 2017 Publication History

Abstract

The huge amount of sensory data collected from mobile devices has offered great potentials to promote more significant services based on user data extracted from sensor readings. However, releasing user data could also seriously threaten user privacy. It is possible to directly collect sensitive information from released user data without user permissions. Furthermore, third party users can also infer sensitive information contained in released data in a latent manner by utilizing data mining techniques. In this paper, we formally define these two types of threats as inherent data privacy and latent data privacy and construct a data-sanitization strategy that can optimize the tradeoff between data utility and customized two types of privacy. The key novel idea lies that the developed strategy can combat against powerful third party users with broad knowledge about users and launching optimal inference attacks. We show that our strategy does not reduce the benefit brought by user data much, while sensitive information can still be protected. To the best of our knowledge, this is the first work that preserves both inherent data privacy and latent data privacy.

References

[1]

Enck W, Gilbert P, Chun B-G, Cox LP, Jung J, McDaniel P, Sheth AN (2010) Taintdroid: an information-flow tracking system for realtime privacy monitoring on smartphones. In: Proceedings of the 9th USENIX conference on operating systems design and implementation, pp 1---6

[2]

http://abcnews.go.com/Technology/gaydar-facebook-friends/story?id=8633224

[3]

Stevens J (1986) Applied multivariate statistics for the social sciences. L. Erlbaum Associates Inc, Hillsdale

[4]

http://sci2s.ugr.es/keel/dataset.php?cod=163

[5]

Shokri R, Theodorakopoulos G, Troncoso C, Hubaux J-P, Le Boudec J-Y (2012) Protecting location privacy: optimal strategy against localization attacks. In: Proceedings of the 2012 ACM conference on computer and communications security, pp 617---627

[6]

Salamatian S, Zhang A, Calmon F, Bhamidipati S, Fawaz N, Kveton B, Oliveira P, Taft N (2013) How to hide the elephant- or the donkey- in the room: practical privacy against statistical inference for large data. In: Global conference on signal and information processing (GlobalSIP), 2013 IEEE, pp 269---272

[7]

Kosinski M, Stillwell D, Graepel T (2013) Private traits and attributes are predictable from digital records of human behavior. Proc Natl Acad Sci 10(15):5802---5805

[8]

Barbaro M, Zeller T (2006) A face is exposed for AOL searcher. N Y Times 9:2008

[9]

Sweeney L (2002) k-anonymity: a model for protecting privacy. Int J Uncertain Fuzziness Knowl Based Syst 10(5):557---570

[10]

Zhang L, Wang X, Lu J, Li P, Cai Z (2016) An efficient privacy preserving data aggregation approach for mobile sensing. Secur Commun Netw 9(16):3844---53

[11]

Zhang L, Cai Z, Wang X (2016) Fakemask: a novel privacy preserving approach for smartphones. IEEE Trans Netw Serv Manag 13(2):335---348

[12]

Wang Y, Cai Z, Ying G, Gao Y, Tong X, Wu G (2016) An incentive mechanism with privacy protection in mobile crowdsourcing systems. Comput Netw 102:157---171

[13]

He Z, Cai Z, Yu J, Wang X, Sun Y, Li Y (2016) Cost-efficient strategies for restraining rumor spreading in mobile social networks. IEEE Trans Veh Technol PP(99):1---1

[14]

Wang Y, Cai Z, Yin G, Gao Y, Tong X, Han Q (2016) A game theory-based trust measurement model for social networks. Comput Soc Netw 3(1):2

[15]

Li N, Li T, Venkatasubramanian S (2007) t-closeness: privacy beyond k-anonymity and l-diversity. In: ICDE, pp 106---115

[16]

Machanavajjhala A, Kifer D, Gehrke J, Venkitasubramaniam M (2007) L-diversity: privacy beyond k-anonymity. ACM Trans Knowl Discov Data 1(1):1---12

[17]

Gruteser M, Grunwald D (2003) Anonymous usage of location-based services through spatial and temporal cloaking. In: Proceedings of the 1st international conference on mobile systems, applications and services, pp 31---42

[18]

Bordenabe NE, Chatzikokolakis K, Palamidessi C (2014) Optimal geo-indistinguishable mechanisms for location privacy. In: Proceedings of the 2014 ACM SIGSAC conference on computer and communications security, ser. CCS '14. ACM, New York, pp 251---262

[19]

Li N, Qardaji W, Su D, Wu Y, Yang W (2013) Membership privacy: a unifying framework for privacy definitions. In: Proceedings of the 2013 ACM SIGSAC conference on computer and communications security, pp 889---900

[20]

Tramer F, Huang Z, Ayday E, Hubaux J-P (2015) Differential privacy with bounded priors: reconciling utility and privacy in genome-wide association studies. In: 22nd ACM conference on computer and communications security

[21]

He Z, Cai Z, Han Q, Tong W, Sun L, Li Y (2016) An energy efficient privacy-preserving content sharing scheme in mobile social networks. Pers Ubiquit Comput 20(5):833---846

[22]

Ioannidis S, Montanari A, Weinsberg U, Bhagat S, Fawaz N, Taft N (2014) Privacy tradeoffs in predictive analytics. SIGMETRICS Perform Eval Rev 42(1):57---69

[23]

Chaabane A, Acs G, Kaafar MA (2012) You are what you like! information leakage through users' interests. In: NDSS

[24]

Fung BCM, Wang K, Chen R, Yu PS (2010) Privacy-preserving data publishing: a survey of recent developments. ACM Comput Surv 42(4):1---14

[25]

Li T, Li N, Zhang J, Molloy I (2012) Slicing: a new approach for privacy preserving data publishing. IEEE Trans Knowl Data Eng 24(3):561---574

[26]

He Z, Cai Z, Li Y (2016) Customized privacy preserving for classification based applications. In: Workshop on privacy-aware mobile computing (PAMCO), pp 37---42

[27]

Han M, Li J, Cai Z, Han Q (2016) Privacy reserved influence maximization in gps-enabled cyber-physical and online social networks. In: The 9th IEEE international conference on social computing and networking

[28]

Allard T, Hébrail G, Masseglia F, Pacitti E (2015) Chiaroscuro: transparency and privacy for massive personal time-series clustering. In: Proceedings of the 2015 ACM SIGMOD international conference on management of data, New York, NY, USA, pp 779---794

[29]

Shokri R, Shmatikov V (2015) Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, pp 1310---1321

[30]

Hu X, Yuan M, Yao J, Deng Y, Chen L, Yang Q, Guan H, Zeng J (2015) Differential privacy in telco big data platform. Proc VLDB Endow 8(12):1692---1703

[31]

Cai Z, He Z, Guan X, Li Y (2016) Collective data-sanitization for preventing sensitive information inference attacks in social networks. IEEE Trans Dependable Secure Comput PP(99):1---1

[32]

Shokri R, Theodorakopoulos G, Le Boudec J-Y, Hubaux J-P (2011) Quantifying location privacy. In: 2011 IEEE symposium on security and privacy (SP), pp 247---262

[33]

Dwork C, McSherry F, Nissim K, Smith A (2006) Calibrating noise to sensitivity in private data analysis. In: Proceedings of the third conference on theory of cryptography, pp 265---284

Cited By

Shen JTian JWang Z(2024)Privacy-preserving algorithm based on vulnerable nodes for social relationshipsThe Journal of Supercomputing10.1007/s11227-024-06308-180:15(22654-22681)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s11227-024-06308-1
Ma CYuan LHan LDing MBhaskar RLi J(2023)Data Level Privacy Preserving: A Stochastic Perturbation Approach Based on Differential PrivacyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.313704735:4(3619-3631)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TKDE.2021.3137047
Sun MTay W(2019)On the Relationship Between Inference and Data Privacy in Decentralized IoT NetworksIEEE Transactions on Information Forensics and Security10.1109/TIFS.2019.292944615(852-866)Online publication date: 8-Oct-2019
https://dl.acm.org/doi/10.1109/TIFS.2019.2929446
Show More Cited By

Customized privacy preserving for inherent data and latent data
1. Security and privacy
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory

Recommendations

Personality-based Knowledge Extraction for Privacy-preserving Data Analysis
K-CAP '17: Proceedings of the 9th Knowledge Capture Conference

In this paper, we present a differential privacy preserving approach, which extracts personality-based knowledge to serve privacy guarantee data analysis on personal sensitive data. Based on the approach, we further implement an end-to-end privacy ...
Privacy-Preserving Data Publishing: An Overview
A review of privacy preserving models for multi-party data release framework
WIR '16: Proceedings of the ACM Symposium on Women in Research 2016

Nowadays, with the improvement of internet technology and advancement in distributed computing data is increasing rapidly. There is a need of information sharing between organizations. Ideally, we wish to share data from multiple private databases and ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Personal and Ubiquitous Computing

Personal and Ubiquitous Computing Volume 21, Issue 1

February 2017

176 pages

ISSN:1617-4909

Issue’s Table of Contents

Copyright © Copyright © 2017 Springer-Verlag London.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 February 2017

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
80
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 25 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Shen JTian JWang Z(2024)Privacy-preserving algorithm based on vulnerable nodes for social relationshipsThe Journal of Supercomputing10.1007/s11227-024-06308-180:15(22654-22681)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s11227-024-06308-1
Ma CYuan LHan LDing MBhaskar RLi J(2023)Data Level Privacy Preserving: A Stochastic Perturbation Approach Based on Differential PrivacyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.313704735:4(3619-3631)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TKDE.2021.3137047
Sun MTay W(2019)On the Relationship Between Inference and Data Privacy in Decentralized IoT NetworksIEEE Transactions on Information Forensics and Security10.1109/TIFS.2019.292944615(852-866)Online publication date: 8-Oct-2019
https://dl.acm.org/doi/10.1109/TIFS.2019.2929446
Wang JTian LHuang YYang DGao H(2018)Achieving the Optimal k-Anonymity for Content Privacy in Interactive Cyberphysical SystemsSecurity and Communication Networks10.1155/2018/79631632018Online publication date: 26-Sep-2018
https://dl.acm.org/doi/10.1155/2018/7963163
Wang JCai ZLi YYang DLi JGao H(2018)Protecting query privacy with differentially private k-anonymity in location-based servicesPersonal and Ubiquitous Computing10.1007/s00779-018-1124-722:3(453-469)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s00779-018-1124-7
Liang YCai ZHan QLi Y(2017)Location Privacy Leakage through Sensory DataSecurity and Communication Networks10.1155/2017/75763072017Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1155/2017/7576307

View Options

View options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents