8,180 research outputs found

    Privacy-Preserving Content-Based Image Retrieval in the Cloud (Extended Version)

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    Storage requirements for visual data have been increasing in recent years, following the emergence of many new highly interactive multimedia services and applications for both personal and corporate use. This has been a key driving factor for the adoption of cloud-based data outsourcing solutions. However, outsourcing data storage to the Cloud also leads to new challenges that must be carefully addressed, especially regarding privacy. In this paper we propose a secure framework for outsourced privacy-preserving storage and retrieval in large image repositories. Our proposal is based on IES-CBIR, a novel Image Encryption Scheme that displays Content-Based Image Retrieval properties. Our solution enables both encrypted storage and searching using CBIR queries while preserving privacy. We have built a prototype of the proposed framework, formally analyzed and proven its security properties, and experimentally evaluated its performance and precision. Our results show that IES-CBIR is provably secure, allows more efficient operations than existing proposals, both in terms of time and space complexity, and enables more reliable practical application scenarios

    Efficient Privacy Preserving Viola-Jones Type Object Detection via Random Base Image Representation

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    A cloud server spent a lot of time, energy and money to train a Viola-Jones type object detector with high accuracy. Clients can upload their photos to the cloud server to find objects. However, the client does not want the leakage of the content of his/her photos. In the meanwhile, the cloud server is also reluctant to leak any parameters of the trained object detectors. 10 years ago, Avidan & Butman introduced Blind Vision, which is a method for securely evaluating a Viola-Jones type object detector. Blind Vision uses standard cryptographic tools and is painfully slow to compute, taking a couple of hours to scan a single image. The purpose of this work is to explore an efficient method that can speed up the process. We propose the Random Base Image (RBI) Representation. The original image is divided into random base images. Only the base images are submitted randomly to the cloud server. Thus, the content of the image can not be leaked. In the meanwhile, a random vector and the secure Millionaire protocol are leveraged to protect the parameters of the trained object detector. The RBI makes the integral-image enable again for the great acceleration. The experimental results reveal that our method can retain the detection accuracy of that of the plain vision algorithm and is significantly faster than the traditional blind vision, with only a very low probability of the information leakage theoretically.Comment: 6 pages, 3 figures, To appear in the proceedings of the IEEE International Conference on Multimedia and Expo (ICME), Jul 10, 2017 - Jul 14, 2017, Hong Kong, Hong Kon

    HIDING BEHIND THE CLOUDS: EFFICIENT, PRIVACY-PRESERVING QUERIES VIA CLOUD PROXIES

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    This project proposes PriView, a privacy-preserving technique for querying third-party ser- vices from mobile devices. Classical private information retrieval (PIR) schemes are diffi- cult to deploy and use, since they require the target service to be replicated and modified. To avoid this problem, PriView utilizes a novel, proxy-mediated form of PIR, in which the client device fetches XORs of dummy query responses from each of two proxies and combines them to produce the required result. Unlike conventional PIR, PriView does not require the third-party service to be replicated or modified in any way. We evaluated a PriView implementation for the Google Static Maps service utilizing an Android OS front- end and Amazon EC2 proxies. PriView is able to provide tunable confidentiality with low overhead, allowing bandwidth usage, power consumption, and end-to-end latency to scale sublinearly with the provided degree of confidentiality
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