Efficient Privacy-Preserving Stream Aggregation in Mobile Sensing With Low Aggregation Error
Aggregate statistics computed from time-series data contributed by individual mobile nodes can be very useful for many mobile sensing applications. Since the data from individual node may be privacy-sensitive, the aggregator should only learn the desired statistics without compromising the privacy of each node. To provide strong privacy guarantee, existing approaches add noise to each node's data and allow the aggregator to get a noisy sum aggregate. However, these approaches either have high computation cost, high communication overhead when nodes join and leave, or accumulate a large noise in the sum aggregate which means high aggregation error.