Cloud computing is an important part of today's world because offloading computations is a method to reduce costs. In this paper, we investigate computing the Speeded Up Robust Features (SURF) using Fully Homomorphic Encryption (FHE). Performing SURF in FHE enables a method to offload the computations while maintaining security and privacy of the original data. In support of this research, we developed a framework to compute SURF via a rational number based compatible with FHE. Although floating point (R) to rational numbers (Q) conversion introduces error, our research provides tight bounds on the magnitude of error in terms of parameters of FHE. We empirically verified the proposed method against a set of images at different sizes and showed that our framework accurately computes most of the SURF keypoints in FHE.
Secret image sharing is a method for distributing a secret image amongst n data stores, each storing a shadow image of the secret, such that the original secret image can be recovered only if any k out of the n shares is available. Existing secret image sharing schemes, however, do not support scaling and cropping operations on the shadow image, which are useful for zooming on large images. In this paper, we propose an image sharing scheme that allows the user to retrieve a scaled or cropped version of the secret image by operating directly on the shadow images, therefore reducing the amount of data sent from the data stores to the user. Results and analyses show that our scheme is highly secure, requires low computational cost, and supports a large number of scale factors with arbitrary crop. We present a theoretical framework for the analysis of privacy and security trade-offs in secure biometric authentication systems. We use this framework to conduct a comparative information-theoretic analysis of two biometric systems that are based on linear error correction codes, namely fuzzy commitment and secure sketches. We derive upper bounds for the probability of false rejection (PFR) and false acceptance (PFA) for these systems. We use mutual information to quantify the information leaked about a user's biometric identity, in the scenario where one or multiple biometric enrollments of the user are fully or partially compromised. We also quantify the probability of successful attack (PSA) based on the compromised information. Our analysis reveals that fuzzy commitment and secure sketch systems have identical PFR, PFA, PSA, and information leakage, but secure sketch systems have lower storage requirements. We analyze both single-factor (keyless) and two-factor (key-based) variants of secure biometrics, and consider the most general scenarios in which a single user may provide noisy biometric enrollments at several access control devices, some of which may be subsequently compromised by an attacker. Our analysis highlights the revocability and reusability properties of key-based systems and exposes a subtle design trade-off between reducing information leakage from compromised systems and preventing successful attacks on systems whose data have not been compromised. Signal processing in the encrypted domain has become a hot research topic, which enable signal processing tasks in a secure and privacy-preserving manner. Taken the fact that SURF (Speeded Up Robust Feature) has been widely utilized in various applications into account, SURF feature extraction method in the encrypted domain has been proposed in this paper. Because all steps must be implemented in the encrypted domain, Paillier homomorphic encryption method is adopted. Experimental results demonstrate that the number and location of SURF features extracted from the encrypted data are the same as those from the plaintext data. And the error between the descriptors obtained from the plaintext data and the encrypted data is only 0.0002932 . We also provide security analysis and complexity analysis. The proposed method can be used in the encrypted domain based applications, such as secure image processing and image retrieval.   In recent years, the paradigm of cloud computing has gained an increasing interest from the academic community as well as from the commercial point of view. The cloud is a very appealing concept both for the providers (who can benefit from hiring out their extra computation and storage resources) and for the users (who can avoid the initial investment on resources by outsourcing their processes and data to a cloud).   In an increasing number of image and video processing problems, cryptographic techniques are used to enforce content access control, identity verification and authentication, and privacy protection. The combination of cryptography and signal processing is an exciting emerging field. This introductory paper gives an overview of approaches and challenges that exist in applying cryptographic primitives to important image and video processing problems, including (partial) content encryption, secure face recognition, and secure biometrics. This paper aims to help the community in appreciating the utility and challenges of cryptographic techniques in image and video processing.