On Binary Decomposition Based Privacy-Preserving Aggregation Schemes in Real-Time Monitoring Systems

In real-time monitoring systems, fine-grained measurements would pose great privacy threats to the participants as real-time measurements could disclose accurate people-centric activities. Differential privacy has been proposed to formalize and guide the design of privacy-preserving schemes. Nonetheless, due to the correlations and high fluctuations in time-series data, it is hard to achieve an effective privacy and utility tradeoff by differential privacy mechanisms. To address this issue, in this paper, we first proposed novel multi-dimensional decomposition based schemes to compress the noise and enhance the utility in differential privacy. The key idea is to decompose the measurements into multi-dimensional records and to achieve differential privacy in bounded dimensions so that the error caused by unbounded measurements can be significantly reduced. We then extended our developed scheme and developed a binary decomposition scheme for privacy-preserving time-series aggregation in real-time monitoring systems. Through a combination of extensive theoretical analysis and experiments, our data shows that our proposed schemes can effectively improve usability while achieving the same level of differential privacy than existing schemes.