With growing popularity of cloud services, the trend in the industry is to outsource the data to a 3rd party system that provides searching in the data as a service. This approach naturally brings privacy concerns about the (potentially sensitive) data. Recently, quite extensive research of outsourcing classic exact-match or keyword search has been done. However, not much attention has been paid to the outsourcing of the similarity search, which becomes more and more important in information retrieval applications. In this work, we propose to the research community a model of outsourcing similarity search to the cloud environment (so called similarity cloud). We establish privacy and efficiency requirements to be laid down for the similarity cloud with an emphasis on practical use of the system in real applications; this requirement list can be used as a general guideline for practical system analysis and we use it to analyze current existing approaches. We propose two new similarity indexes that ensure data privacy and thus are suitable for search systems outsourced in a cloud. The balance of the first proposed technique EM-Index is more on the efficiency side while the other (DSH Index) shifts this balance more to the privacy side.
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