With the interaction of geographic data and social data, the inference attack has been mounting up, calling for new technologies for privacy protection. Although there are many tangible contributions of spatial-temporal cloaking technologies, traditional technologies are not enough to resist privacy intrusion. Malicious attackers still steal user-sensitive information by analyzing the relationship between location and query semantics. Reacting to many interesting issues, oblivious transfer (OT) protocols are introduced to guarantee location privacy. To our knowledge, OT is a cryptographic primitive between two parties and can be used as a building block for any arbitrary multiparty computation protocol. Armed with previous privacy-preserving technologies, for example, OT, in this work, we first develop a novel region queries framework that can provide robust privacy for location-dependent queries. We then design an OT-assist privacy-aware protocol (or OTPA) for location-based service with rigorous security analysis. In short, the common query of the client in our solution can be divided into two parts, the region query
R
q
and the content query
C
q
, to achieve location
k
-anonymity, location
m
-diversity, and query
r
-diversity, which ensure the privacy of two parties (i.e., client and server). Lastly, we instantiate our OTPA protocol, and experiments show that the proposed OTPA protocol is reasonable and effective.
Enriching query semantics for code search with reinforcement learning