Differentially private aggregation of distributed time-series with transformation and encryption

We propose PASTE, the first differentially private aggregation algorithms for distributed time-series data that offer good practical utility without any trusted server. PASTE addresses two important challenges in participatory data-mining applications where (i) individual users collect temporally correlated time-series data (such as location traces, web history, personal health data), and (ii) an untrusted third-party aggregator wishes to run aggregate queries on the data. To address this, PASTE incorporates two new algorithms. To ensure differential privacy for time-series data despite the presence of temporal correlation, PASTE uses the Fourier Perturbation Algorithm (FPAk). Standard differential privacy techniques perform poorly for time-series data. To answer n queries, such techniques can result in a noise of Θ(n) to each query answer, making the answers practically useless if n is large. Our FPAk algorithm perturbs the Discrete Fourier Transform of the query answers. For answe...

• Type: Conference
• Year: 2010
• Where: SIGMOD
• Authors: Vibhor Rastogi, Suman Nath
• Keywords: Correlated Time-series Data, Database, Query Answers, SIGMOD 2010, Time-series Data