Privacy-Preserving Aggregation of Time-Series Data
Source: University of Hong Kong
The authors consider how an untrusted data aggregator can learn desired statistics over multiple participants' data, without compromising each individual's privacy. They 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. They achieve strong privacy guarantees using two main techniques. First, they show how to utilize applied cryptographic techniques to allow the aggregator to decrypt the sum from multiple ciphertexts encrypted under different user keys.