Informative Yet Unrevealing

Abstract

The preservation of geo-privacy is a critical consideration for location-based service (LBS) providers. Unfortunately, a trade-off typically exists between the quality of location-based services and revealing of private information (e.g. geo-coordinates) to obtain such services. In this work, we develop semantic obfuscation methods, which allow a trusted third-party to convert revealing geo-coordinates into highly anonymous semantic features. Following, LBS providers can operate directly via location semantics to deliver the necessary services. Using a large-scale travel survey dataset, we evaluate our obfuscation approach while considering a common user-intention prediction problem. Our results demonstrate that our approach is capable of significantly obfuscating user location while maintaining LBS quality. On average, we show that the k-anonymity measure increases by 15.22 times while the quality of prediction drops only 3.24%.