Secured histories: computing group statistics on encrypted data while preserving individual privacy

TL;DR

This paper introduces CollaPSE security, enabling privacy-preserving computation of group statistics on encrypted presence data, balancing individual privacy with hierarchical access control in sensor-based systems.

Contribution

It proposes a novel security model for encrypted data processing that supports hierarchical access and implements practical protocols for time series data.

Findings

Efficient cryptographic protocols for CollaPSE security.

Successful integration with presence system at FXPAL.

Preserves privacy while enabling hierarchical data analysis.

Abstract

As sensors become ever more prevalent, more and more information will be collected about each of us. A longterm research question is how best to support beneficial uses while preserving individual privacy. Presence systems are an emerging class of applications that support collaboration. These systems leverage pervasive sensors to estimate end-user location, activities, and available communication channels. Because such presence data are sensitive, to achieve wide-spread adoption, sharing models must reflect the privacy and sharing preferences of the users. To reflect users' collaborative relationships and sharing desires, we introduce CollaPSE security, in which an individual has full access to her own data, a third party processes the data without learning anything about the data values, and users higher up in the hierarchy learn only statistical information about the employees under them. We describe simple schemes that efficiently realize CollaPSE security for time series data. We implemented these protocols using readily available cryptographic functions, and integrated the protocols with FXPAL's myUnity presence system.