Privacy Preserving Similarity Evaluation of Time Series Data

Privacy preserving issues of time series databases in financial, medical and transportation applications have become more and more important recently. A key problem in time series databases is to compute the similarity between two different time series. Despite some recent work on time series security and privacy, there is very limited progress on securely computing the similarity between two time series. In this paper, we consider exactly this problem in a twoparty setting (client and server). In particular, we want to compute the similarity between two time series, one from the client and the other from the server, without revealing the actual time series to the other party. Only the value of the similarity should be revealed to both parties at the end. At the same time, we want to do the computation as efficiently as possible. Therefore, we propose practical protocols for computing the similarity (or distance) for time series using two popular and well known functions: Dynamic