Title: Communication-Efficient and Privacy-Preserving Data Aggregation without Trusted Authority
Time: 2018年10月18日上午9：30 - 10：30
Privacy-preserving data aggregation has been extensively studied in the past decades. However, most of these works target at specific aggregation functions such as additive or multiplicative aggregation functions. Meanwhile, they assume there exists a trusted authority which facilitates the keys and other information distribution. In this paper, we aim to devise a communication efficient and privacy-preserving protocol that can exactly compute arbitrary data aggregation functions without trusted authority. In our model, there exist one untrusted aggregator and participants. We assume that all communication channels are insecure and are subject to eavesdropping attacks. Our protocol is designed under the semi-honest model, and it can also tolerate to collusive adversaries. Our protocol achieves -source anonymity. That is, for the source of each collected data aparting from the colluded participants, what the aggregator learns is only from one of the noncolluded ones. Compared with recent work  that computes arbitrary aggregation functions by collecting all the participants’ data using the trusted authority, our protocol increases merely by at most a factor of in terms of computation time and communication cost. The key of our protocol is that we have designed algorithms that can efficiently assign unique sequence numbers to each participant without the trusted authority. Then, we consider a practical scenario where participants may join or leave in the system model. We propose efficient protocols for both dynamic join and leave. Our protocols utilize a basic oblivious transfer scheme and the existing unique sequence numbers of the existing participants to reduce the communication cost.