Secure Surfing: Privacy-Preserving Speeded-Up Robust Feature Extractor

Large-scale multimedia data are being exponentially generated, stored and processed continuously nowadays. Along with the data explosion, the data owner is highly motivated to outsource his/her huge amount of data storage and computation-expensive processing jobs to the cloud by leveraging its abundant resources for cost reduction and flexibility. Despite the fascinating advantages, security and privacy concerns are the primary obstacles that prevent the wide adoption of this promising information technology paradigm. In this work, we aim at outsourcing Speeded-up Robust Features (SURF), a widely-used feature extraction algorithm, to the intrinsically untrusted cloud, while protecting data owner's private information on the outsourced image data. We, for the first time, propose a practical privacy-preserving outsoucing scheme for SURF that can preserve its key characteristics in terms of distinctiveness and robustness. By randomly splitting the original image data and distributing the encrypted data shares to two independent cloud servers, we first design two novel efficient protocols for secure multiplication and comparison by leveraging somewhat homomorphic encryption (SHE) and single-instruction multiple-data (SIMD). We then carefully tune every step of the original SURF to adapt it to the ciphertext domain. A thorough theoretical analysis of effectiveness and security shows that our scheme is practically secure and approximates well the performance of the original SURF executed in the plaintext domain. Extensive experiments are also conducted over real-world image datasets to show that our scheme outperforms the existing solution and indeed performs comparably to the original SURF in preserving its various characteristics including image scale invariance, rotation invariance and robust matching across 3D viewpoint change etc.