Research map / Learning theory and algorithms / Prediction and decision losses
Pacmann: Efficient Private Approximate Nearest Neighbor Search
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Proposes Pacmann, a private approximate nearest neighbor search scheme that hides the client's query vector from the server by offloading limited computation and storage to the client instead of running heavy cryptographic search on the server.
Problem
Private approximate nearest neighbor (ANN) search in a vector database, where the client must query without revealing the query vector to the server; prior constructions rely on computationally expensive server-side encrypted search.
Approach
Offload limited computation and storage to the client so that the server no longer needs computationally intensive cryptographic techniques for private ANN search.
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