Differential Privacy Preservation for Continuous Release of Real-Time Location Data

TL;DR

This paper introduces a new method to protect location privacy in real-time data while maintaining data usefulness.

Contribution

The novel QCLM-Lowpass method improves differential privacy for continuous location data by addressing nonstationary correlations and filter deviations.

Findings

Location increment-based correlation estimation handles nonstationary data effectively.

QCLM-Lowpass reduces output deviations in time-varying environments.

Experiments show QCLM-Lowpass balances privacy and data availability better than existing methods.

Abstract

Continuous real-time location data is very important in the big data era, but the privacy issues involved is also a considerable topic. It is not only necessary to protect the location privacy at each release moment, but also have to consider the impact of data correlation. Correlated Laplace Mechanism (CLM) is a sophisticated method to implement differential privacy on correlated time series. This paper aims to solve the key problems of applying CLM in continuous location release. Based on the finding that the location increment is approximately stationary in many scenarios, a location correlation estimation method based on the location increment is proposed to solve the problem of nonstationary location data correlation estimation; an adaptive adjustment model for the CLM filter based on parameter quantization idea (QCLM) as well as its effective implementation named QCLM-Lowpass utilizing the lowpass spectral characteristics of location data series is proposed to solve the problem of output deviations due to the undesired transient response of the CLM filter in time-varying environments. Extensive simulations and real data experiments validate the effectiveness of the proposed approach and show that the privacy scheme based on QCLM-Lowpass can offer a better balance between the ability to resist correlation-based attacks and data availability.

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