Privacy-Preserving Stream Aggregation With Fault Tolerance
Source: University of Hong Kong
The authors consider applications where an untrusted aggregator would like to collect privacy sensitive data from users, and compute aggregate statistics periodically. For example, imagine a smart grid operator who wishes to aggregate the total power consumption of a neighborhood every ten minutes; or a market researcher who wishes to track the fraction of population watching ESPN on an hourly basis. They design novel mechanisms that allow an aggregator to accurately estimate such statistics, while offering provable guarantees of user privacy against the untrusted aggregator.