Repetitive, Oblivious, and Unlinkable SkNN Over Encrypted-and-Updated Data on Cloud

Meng Li, Mingwei Zhang, Jianbo Gao, Chhagan Lal, Mauro Conti, Mamoun Alazab

Abstract: Location-Based Services (LBSs) depend on a Service Provider (SP) to store data owners’ geospatial data and to process data users’ queries. For example, a Yelp user queries the SP to retrieve the k nearest Starbucks by submitting her/his current location. It is well-acknowledged that location privacy is vital to users and several prominent Secure k Nearest Neighbor (SkNN) query processing schemes are proposed. We observe that no prior work addresses the requirement of repetitive query after index update and its privacy issue, i.e., how to match a data item from the cloud repetitively in an oblivious and unlinkable manner. Meanwhile, a malicious SP may skip some data items and recommend others due to unfair competition.