Abstract. Signal processing governs almost every audiovisual stimuli that we receive from electronic sources. Recently, concerns about privacy of theprocessed signals (especially biomedical signals) has beenraised, as it has been traditionally overlooked. This fact, together with the advent of Cloud computing and the growing tendency to outsource not only the storage but also the processing of data has created a fundamental need for privacy preserving techniques that protect signals at the Cloud. We provide a landscape of technologies brought up by the novel discipline of Signal Processing in the Encrypted Domain (SPED), and we show their application to solve Cloud Computing privacy issues, introducing the concept of virtualized CryptoDSPs, as an architecture for implementing SPED technologies on Cloud scenarios.

Cloud security comprises access control and end-to-end security based on flow or message-level privacy. In some applications, in which all processing takes place at the client side and the Cloud simply handles data storage (e.g. Google Docs), on-line data encryption/decryption guarantees privacy. However, when a service requires server processing (e.g. spreadsheets), privacy must necessarily rely on a dependable entity according to local regulations. Summing up, full Cloud privacy has not been achieved so far. In this paper we take a step towards that goal. We propose executing server side operations in the encrypted domain, so that both the operands and the results are opaque to the server, yet clear to the user. We evaluate this concept with a real Google Apps implementation of basic arithmetic operations. 1

Abstract. Motivated by the need to outsource file storage to untrusted clouds while still permitting limited use of that data by third parties, we present practical protocols by which a client (the third-party) can evaluate a deterministic finite automaton (DFA) on an encrypted file stored at a server (the cloud), once authorized to do so by the file owner. Our protocols provably protect the privacy of the DFA and the file contents from a malicious server and the privacy of the file contents (except for the result of the evaluation) from an honest-but-curious client (and, heuristically, from a malicious client). We further present simple techniques to detect client or server misbehavior. 1