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

EVENT:

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

NDSS 2011

6 February 2011
San Diego, California

 

description

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 

Human-Centered Designed Robots are All Around Us
Leila Takayama
29 September 2016
PARC Forum  

Stanford and PARC host Digital Cities Summit 2016
Victoria Bellotti, Panel Facilitator, Bernard Casse, Panelist, Sean Garner, Panelist, Stephen Hoover, Keynote Speaker, Matthew Klenk, Tolga Kurtoglu, Panel Facilitator, Markus Larsson, Speaker, Ersin Uzun, Panelist
3 October 2016 - 4 October 2016 | Stanford, CA
Conferences & Talks  

2016 Annual Conference of the Prognostics and Health Management Society
Parham Shahidi, Panelist, Rui Maranhão, Keynote Speaker
5 October 2016 - 6 October 2016 | Denver, CO
Conferences & Talks