Sciweavers

Share
SIGMOD
2010
ACM

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

10 years 2 months ago
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...
Vibhor Rastogi, Suman Nath
Added 18 Jul 2010
Updated 18 Jul 2010
Type Conference
Year 2010
Where SIGMOD
Authors Vibhor Rastogi, Suman Nath
Comments (0)
books