PriSense: Privacy-preserving data aggregation in people-centric urban sensing systems
Document Type
Conference Proceeding
Publication Date
6-15-2010
Abstract
People-centric urban sensing is a new paradigm gaining popularity. A main obstacle to its widespread deployment and adoption are the privacy concerns of participating individuals. To tackle this open challenge, this paper presents the design and evaluation of PriSense, a novel solution to privacy-preserving data aggregation in people-centric urban sensing systems. PriSense is based on the concept of data slicing and mixing and can support a wide range of statistical additive and non-additive aggregation functions such as Sum, Average, Variance, Count, Max/Min, Median, Histogram, and Percentile with accurate aggregation results. PriSense can support strong user privacy against a tunable threshold number of colluding users and aggregation servers. The efficacy and efficiency of PriSense are confirmed by thorough analytical and simulation results. ©2010 IEEE.
Identifier
77953308558 (Scopus)
ISBN
[9781424458363]
Publication Title
Proceedings IEEE INFOCOM
External Full Text Location
https://doi.org/10.1109/INFCOM.2010.5462147
ISSN
0743166X
Recommended Citation
Shi, Jing; Zhang, Rui; Liu, Yunzhong; and Zhang, Yanchao, "PriSense: Privacy-preserving data aggregation in people-centric urban sensing systems" (2010). Faculty Publications. 6241.
https://digitalcommons.njit.edu/fac_pubs/6241
