Privacy-Preserving Outsource Computing for Binary Vector Similarity

The preservation of privacy has become a widely discussed topic on the Internet. Encryption is an approach to privacy; however, to outsource computing to an cloud service without revealing private information over encrypted data is difficult. Homomorphic encryption can contribute to it but is based on complicated mathematical structures of abstract algebra. We propoase a new scheme for securely computing the similarity between binary vectors through a cloud server. The scheme is constructed from ciphertext policy attribute based encryption and garbled circuits rather than homomorphic encryption. Attribute based encryption provides the access power, which is a necessary primitive in our scheme. Moreover, for computing over encrypted data, we rely on garbled circuits to handle secure outsourcing and to avoid the use of homomorphic encryption.