9 citations · 27 across the 10 of their papers we have counts for
10 papers
DPIVE: A Regionalized Location Obfuscation Scheme with Personalized Privacy Levels
Shun Zhang, Pengfei Lan, Benfei Duan +3
The popularity of cyber-physical systems is fueling the rapid growth of location-based services. This poses the risk of location privacy disclosure. Effective privacy preservation…
On the differential privacy of dynamic location obfuscation with personalized error bounds
Zhang Shun, Duan Benfei, Chen Zhili +1
Geo-indistinguishability and expected inference error are two complementary notions for location privacy. The joint guarantee of differential privacy (indistinguishability) and dis…
Utility-efficient Differentially Private K-means Clustering based on Cluster Merging
Tianjiao Ni, Minghao Qiao, Zhili Chen +2
Differential privacy is widely used in data analysis. State-of-the-art -means clustering algorithms with differential privacy typically add an equal amount of noise to centroids…
A Differentially Private Framework for Spatial Crowdsourcing with Historical Data Learning
Shun Zhang, Benfei Duan, Zhili Chen +2
Spatial crowdsourcing (SC) is an increasing popular category of crowdsourcing in the era of mobile Internet and sharing economy. It requires workers to arrive at a particular locat…
Differentially Private Combinatorial Cloud Auction
Tianjiao Ni, Zhili Chen, Lin Chen +3
Cloud service providers typically provide different types of virtual machines (VMs) to cloud users with various requirements. Thanks to its effectiveness and fairness, auction has…
Differentially Private Aggregated Mobility Data Publication Using Moving Characteristics
Zhili Chen, Xiaoli Kan, Shun Zhang +3
With the rapid development of GPS enabled devices (smartphones) and location-based applications, location privacy is increasingly concerned. Intuitively, it is widely believed that…
Differentially Private User-based Collaborative Filtering Recommendation Based on K-means Clustering
Zhili Chen, Yu Wang, Shun Zhang +2
Collaborative filtering (CF) recommendation algorithms are well-known for their outstanding recommendation performances, but previous researches showed that they could cause privac…
Probabilistic Matrix Factorization with Personalized Differential Privacy
Shun Zhang, Laixiang Liu, Zhili Chen +1
Probabilistic matrix factorization (PMF) plays a crucial role in recommendation systems. It requires a large amount of user data (such as user shopping records and movie ratings) t…
Making Double Spectrum Auction Practical: Both Privacy and Efficiency Matter
Zhili Chen, Xuemei Wei, Hong Zhong +3
Truthful spectrum auction is believed to be an effective method for spectrum redistribution. However, privacy concerns have largely hampered the practical applications of truthful…
Differentially Private Double Spectrum Auction with Approximate Social Welfare Maximization
Zhili Chen, Tianjiao Ni, Hong Zhong +2
Spectrum auction is an effective approach to improving spectrum utilization, by leasing idle spectrum from primary users to secondary users. Recently, a few differentially private…