Equivalent mechanism: Releasing location data with errors through differential privacy

Abstract

Location-based services are raising remarkable convenience to our daily life while seriously threatening the location privacy of individuals. Differential privacy provides a promising privacy definition for location data. It is enforced by injecting random noise into each location such that the level of privacy and utility provided by this sanitization when querying an LBS is quantified and controlled. However, the primitive differential privacy overlooks data errors, which constantly exist in real-life location data, thereby potentially deviating a specified indistinguishability. Therefore, we determine the impact of data errors on the indistinguishability to address the abovementioned issue. Then, we design an equivalent mechanism to enforce differential privacy and analyze its privacy and utility. Extensive experimental evaluation on real-world datasets demonstrates that our proposed equivalent mechanism consistently outperforms several state-of-the-art mechanisms in data utility at the same privacy level.