Privacy-preserving outsourcing of image global feature detection
The amount and availability of user-contributed image data have been dramatically increased during the past ten years. Popular multimedia social networks, e.g. Flicker, commonly utilize user image data to construct user behavior models, social preferences, etc., for the purpose of effective advertisement, better user retention and attraction, and many others. Existing practices of data utilization, however, seriously deteriorate users' personal privacy and have led to increasing criticisms and legislation pressures. In this paper, we aim to construct a privacy-preserving feature detection scheme over encrypted image data. The proposed system enables an interested party to perform a variety of image feature detection tasks, including visual descriptors in MPEG-7 standard, while protecting user privacy relating to image contents. We implement a prototype system based on somewhat homomorphic encryption scheme and the benchmark Caltech256 database. The experimental results show that our system can guarantee effective image feature detection without sacrificing user privacy.