On binary decomposition based privacy-preserving aggregation schemes in real-time monitoring systems

Real-time monitoring systems can introduce numerous benefits to the participants in terms of performing data mining and analysis. Nonetheless, due to the correlations in time-series data, it is hard to achieve an effective privacy and utility tradeoff through a normal differential privacy mechanism. To address this issue, we propose novel multi-dimensional decomposition based schemes, which can greatly improve the utility in differential privacy. After extending the developed scheme, we then develop a binary decomposition scheme for time-series aggregation in real-time monitoring systems. Through both 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.