Privacy-Preserving Identification via Layered Sparse Code Design: Distributed Servers and Multiple Access Authorization

We propose a new computationally efficient privacy-preserving identification framework based on layered sparse coding. The key idea of the proposed framework is a sparsifying transform learning with ambiguization, which consists of a trained linear map, a component-wise nonlinearity and a privacy amplification. We introduce a practical identification framework, which consists of two phases: public and private identification. The public untrusted server provides the fast search service based on the sparse privacy protected codebook stored at its side. The private trusted server or the local client application performs the refined accurate similarity search using the results of the public search and the layered sparse codebooks stored at its side. The private search is performed in the decoded domain and also the accuracy of private search is chosen based on the authorization level of the client. The efficiency of the proposed method is in computational complexity of encoding, decoding, "encryption" (ambiguization) and "decryption" (purification) as well as storage complexity of the codebooks.Comment: EUSIPCO 201

Taxonomy of Technological IT Outsourcing Risks: Support for Risk Identification and Quantification

The past decade has seen an increasing interest in IT outsourcing as it promises companies many economic benefits. In recent years, IT paradigms, such as Software-as-a-Service or Cloud Computing using third-party services, are increasingly adopted. Current studies show that IT security and data privacy are the dominant factors affecting the perceived risk of IT outsourcing. Therefore, we explicitly focus on determining the technological risks related to IT security and quality of service characteristics associated with IT outsourcing. We conducted an extensive literature review, and thoroughly document the process in order to reach high validity and reliability. 149 papers have been evaluated based on a review of the whole content and out of the finally relevant 68 papers, we extracted 757 risk items. Using a successive refinement approach, which involved reduction of similar items and iterative re-grouping, we establish a taxonomy with nine risk categories for the final 70 technological risk items. Moreover, we describe how the taxonomy can be used to support the first two phases of the IT risk management process: risk identification and quantification. Therefore, for each item, we give parameters relevant for using them in an existing mathematical risk quantification model

Successive Refinement with Decoder Cooperation and its Channel Coding Duals

We study cooperation in multi terminal source coding models involving successive refinement. Specifically, we study the case of a single encoder and two decoders, where the encoder provides a common description to both the decoders and a private description to only one of the decoders. The decoders cooperate via cribbing, i.e., the decoder with access only to the common description is allowed to observe, in addition, a deterministic function of the reconstruction symbols produced by the other. We characterize the fundamental performance limits in the respective settings of non-causal, strictly-causal and causal cribbing. We use a new coding scheme, referred to as Forward Encoding and Block Markov Decoding, which is a variant of one recently used by Cuff and Zhao for coordination via implicit communication. Finally, we use the insight gained to introduce and solve some dual channel coding scenarios involving Multiple Access Channels with cribbing.Comment: 55 pages, 15 figures, 8 tables, submitted to IEEE Transactions on Information Theory. A shorter version submitted to ISIT 201

Successive Refinement of Shannon Cipher System Under Maximal Leakage

We study the successive refinement setting of Shannon cipher system (SCS) under the maximal leakage constraint for discrete memoryless sources under bounded distortion measures. Specifically, we generalize the threat model for the point-to-point rate-distortion setting of Issa, Wagner and Kamath (T-IT 2020) to the multiterminal successive refinement setting. Under mild conditions that correspond to partial secrecy, we characterize the asymptotically optimal normalized maximal leakage region for both the joint excess-distortion probability (JEP) and the expected distortion reliability constraints. Under JEP, in the achievability part, we propose a type-based coding scheme, analyze the reliability guarantee for JEP and bound the leakage of the information source through compressed versions. In the converse part, by analyzing a guessing scheme of the eavesdropper, we prove the optimality of our achievability result. Under expected distortion, the achievability part is established similarly to the JEP counterpart. The converse proof proceeds by generalizing the corresponding results for the rate-distortion setting of SCS by Schieler and Cuff (T-IT 2014) to the successive refinement setting. Somewhat surprisingly, the normalized maximal leakage regions under both JEP and expected distortion constraints are identical under certain conditions, although JEP appears to be a stronger reliability constraint

Cloudlet-based just-in-time indexing of IoT video

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