home › event - privacy-preserving aggregation of time-series data


Privacy-preserving aggregation of time-series data
Conferences & Talks

NDSS 2011

6 February 2011
San Diego, California



We consider how an untrusted data aggregator can learn desired statistics over multiple participants’ data, without compromising each individual’s privacy. We propose a construction that allows a group of participants to periodically upload encrypted values to a data aggregator, such that the aggregator is able to compute the sum of all participants’ values in every time period, but is unable to learn anything else. We achieve strong privacy guarantees using two main techniques. First, we show how to utilize applied cryptographic techniques to allow the aggregator to decrypt the sum from multiple ciphertexts encrypted under different user keys. Second, we describe a distributed data randomization procedure that guarantees the differential privacy of the outcome statistic, even when a subset of participants might be compromised.


upcoming events   view all 

The Experience When Business Meets Design
Brian Solis
27 October 2016
PARC Forum  

AI Case Studies: Pushing the Frontiers of Systems Engineering
Tolga Kurtoglu
9 November 2016 | San Francisco, CA
Conferences & Talks  

2016 AIChE Annual Meeting: Energy and Transport Processes
Corie L. Cobb
14 November 2016 - 15 November 2016 | San Francisco, CA
Conferences & Talks  

Printed Electronics USA 2016 - Visit PARC’s Booth #T20
Ross Bringans, Markus Larsson, Janos Veres
16 November 2016 - 17 November 2016 | Santa Clara, CA
Conferences & Talks