Big Data Privacy

With the high-speed development of information and network, big data has become a hot topic in both the academic and industrial research, which is regarded as a new revolution in the field of Information Technology. However, it brings about not only significant economic and social benefits, but also great risks and challenges on individuals’privacy protection and data security. Directly releasing and analyzing such data may breach individuals’ privacy. Currently, privacy related with big data has been considered as one of the greatest problems in many applications.

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Research Work
  • Location privacy preserving technique

    With the blooming of sensor and wireless mobile devices, location-based services become more and more valuable and important, such as location-based emergency services and advertising services. People are aware that personal privacy and security are threatened when they enjoy a tempting location-based service. Based on location type, query type and user credibility, we studied the key issues of location privacy-preserving technology, designed and developed the privacy-preserving prototyping system—OrientPrivacy, also provided viable solutions for privacy-preserving technology in location services.

  • Trajectory privacy preserving technique

    With the development of mobile positioning techniques, various location-aware devices appear and numerous location data are collected. Trajectories are location data ordered by the sapmpling time. Although mining and analyzing trajectories is beneficial to multiple mobility-related applications, it still causes serious threats to personal privacy. We study privacy preserving algorithms and data utility measurement and address several key issues in privacy preserving techniques against different kinds of attack models.

  • Privacy protection in data publication and analysis

    Information techniques have enabled many organizations to easily collect vast amount of personal data. Release and analysis on such data can potentially support various application requirements such as diseases evolution and consumer behaviors insight. A challenge to analysis and publication is to protect private personal data and prevent sensitive information from disclosure. However, most existing methods based on k-anonymity or partition-based have serious limitations because they only preserve individual privacy under special assumption of adversary’s background knowledge. Differential privacy has emerged as a new paradigm for privacy protection with strong privacy guarantees against adversaries with arbitrary background knowledge. Differential privacy has recently emerged as a de facto standard for statistical analysis, and become a hot topic in the academic. The main idea of differential privacy is to use noisy mechanisms to protect individuals’ privacy.

  • Quantifying privacy risk of mobile users
    Mobile privacy risk refers to the privacy risk that occurs when personal data is collected by other parties in the use of mobile devices. We mainly focus on the potential privacy risk of Android system, a free and open-sourced operating system. Since Android platform allows users to install all kinds of APP files downloaded from various applicaition markets and websites. When people use some APPs, personal data may be obtained by applicaiton developers and other third-parties. How to measure personal privacy risk at this time and put forward corresponding privacy protection methods are the main problem we study.

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  • System
  • Orient Differential Privacy

    OrientDP - a Differential Privacy demo system based on the interactive frame, shows the two most widely used Differential Privacy preservation mechanism in research - The Laplace Mechanism and The Exponential Mechanism. Besides. It also gives application examples of DP in the area of Intelligent Traffic and WIT120(Wise Information Technology of 120). This system is of value and reference for non-professionals to understand the Differential Privacy mechanism.

  • AppPrivacy: App Privacy Risk Accessment System
    With the rapid development of mobile systems and application markets, users are faced with huge personal privacy risks while enjoying the services provided by various APPs. At present, privacy group of WAMDM lab mainly focuses on privacy risk quantification and active privacy preserving, then propose a quantificatioin model of personal privacy risk on mobile platform. Based on this, we have designed and implement an App privacy risk accessment system, AppPrivacy, which can simulate user's operations with some apps, monitor user behaviors, quantify personal privacy risk and provide early warning.

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  • Publications
    • S. Guo, X. Meng. Density Peaks Clustering with Differential Privacy[C]. Proceedings of the 8th Biennial Conference on Innovative Data Systems Research(CIDR),2017, Chaminade, CA.
    • X. Zhang, C. Shao, X. Meng. Accurate Histpgram Release under Differential Privacy[J].Journal of Computer Research and Development, Vol 53 (05):1106-1117,2016.
    • L. Wang, X. Meng, S. Guo.Preservation of Implicit Privacy in Spatio-Temporal Data Publication[J].Journal of Software, Vol 27(08):1922-1933,2016.
    • X. Zhang, X. Meng.Streaming Histogram Publication Method with Differential Privacy. Journal of Software, Vol 27(02):381-393,2016.
    • L. Wang, R. Ma, X. Meng: Evaluating k Nearest Neighbor Query on Road Networks with no Information Leakage[C]. Proceedings of the 16th International Conference on Web Information Systems Engineering (WISE), pages:508-521, 2015, Miami, Florida.
    • L.Wang, X.Meng, Hu H, et al. Bichromatic Reverse Nearest Neighbor Query Without Information Leakage[C]. Proceedings of the20th International Conferenceon Database Systems for Advanced Applications (DASFAA),pages:609-624,2015,Hanoi, Vietnam.
    • X. Meng, X. Zhang. Big Data Privacy Management[J]. Journal of Computer Research and Development,2015,02:265-281.[PDF]
    • X. Zhang, R. Chen, J. Xu, X. Meng. Towards Accurate Histogram Publication under Differential Privacy. Accepted for publication in Proceedings of the 14th SIAM International Conference on Data Mining (SDM 2014): 587-595, Philadelphia, Pennsylvania, USA. (Full Paper)
    • X. Zhang, X. Meng. Discovering top-k patterns with differential privacy-an accurate approach. Frontiers of Computer Science. Vol 8(5): 816-827, 2014.
    • X. Zhang, X. Meng: Differential Privacy in Data Publication and Analysis. Chinese Journal of Computers. Vol 37(4):927-949, 2014, 4.
    • X. Zhang, M. Wang, X. Meng: An Accurate Method for Mining top-k Frequent Pattern under Differential Privacy. Journal of Computer Research and Development. Vol 51(1): 104-114, 2014, 1.
    • L. Wang, X. Meng: Location Privacy Preservation in Big Data Era: A Survey. Journal of Software. Vol 25(4): 693-712, 2014.
    • X. Zhang, X. Meng, R. Chen: Differential Private Set-Valued Data Release against Incremental Updates. In Proceedings of the 18th International Conference on Database Systems for Advanced Applications (DASFAA 2013): 392-406. April 22-25, 2013, Wuhan, China. (Regular paper)
    • X. Pan, X. Meng: Preserving location privacy without exact locations in mobile services. Frontiers of Computer Science Vol.7(3): 317-340 , 2013.
    • Z. Huo, X. Meng, R. Zhang: Feel Free to Check In: Privacy Alert against Hidden Location Inference Attacks in GeoSN. In Processings of the 18th International Conference on Database Systems for Advanced Applications (DASFAA 2013): 377-491. April 22-25, 2013, Wuhan, China. (Regular paper)
    • Z. Huo, X. Meng, H. Hu, Y. Huang: You Can Walk Alone: Trajectory Privacy-Preserving through Significant Stays Protection. In Proceedings of the 17th International Conference of Database Systems for Advanced Applications (DASFAA 2012), pages: 351-366, April 15-19, 2012, Busan, South Korea.
    • X. Pan, J. Xu, X. Meng: Protecting Location Privacy Against Location-dependent Attacks in Mobile Services. IEEE Transaction on Knowledge and Data Engineering (TKDE).24(8):1506-1519,2012 .(Regular paper)
    • Z. Huo, Y. Huang, X. Meng: History Trajectory Privacy-preserving through Graph Partition. In Proceedings of the 1st International Workshop on Mobile Location-Based Service (MLBS 2011, in conjunction with Ubicomp). September 17-21, Beijing, 2011: 71-78 .
    • Z. Huo, X. Meng: A Survey of Trajectory Privacy-Preserving Techniques. Chinese Journal of Computers, Vol 34(10):1820-1830, 2011,10.
    • X. Pan, X. Hao, X. Meng: Privacy Preserving Towards Continuous Query in Location-Based Services. Journal of Computer Research and Development, Vol.47(1): 121-129, 2010.1.
    • X. Pan, X. Meng, J. Xu: Distortion-based Anonymity for Continuous Query in Location-Based Mobile Services. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS 2009):256-265, November 4-6, 2009, Seattle, Washington. (FULL paper)
    • X. Min, H. Wang, J. Yin, X. Meng: Providing Freshness Guarantees for Outsourced Databases. In Proceedings of the 11th International Conference on Extending Database Technology(EDBT2008), page 323-332, Nantes, France, March 25-30, 2008. ( Full paper)
    • X. Min, H. Wang, J. Yin, X. Meng: Integrity Auditing of Outsourced Data. In Proceedings of the 33th International Conference on Very Large Data Bases(VLDB2007), pages 782-793, Vienner, Austria, September 24-28, 2007.
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    Reference
    • PScout. http://pscout.csl.toronto.edu/
    • Harvard University Privacy Tools Project. https://privacytools.seas.harvard.edu/differential-privacy
    • PrivMetrics. http://www.privmetrics.org/
    • PINQ. https://www.microsoft.com/en-us/research/project/privacy-integrated-queries-pinq/?from=http%3A%2F%2Fresearch.microsoft.com%2Fen-us%2Fprojects%2Fpinq%2Ftutorial.aspx
    • Airavat. http://z.cs.utexas.edu/users/osa/airavat/
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