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 

What is the Industrial Internet of Things (IIoT)?
Aki Ohashi
19 September 2017 | Torrance, CA
Conferences & Talks  

What is the Future of Cybersecurity?
Alissa Johnson
26 September 2017 | George E. Pake Auditorium, PARC
PARC Forum