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   https://doi.org/10.1109/TNET.2022.3230009  

   Tsai, Cho-Hsin ; Wang, Chih-Chun ( January 2023 , IEEE/ACM Transactions on Networking)

   The ever-increasing needs of supporting real-time applications have spurred new studies on minimizing Age-of-Information (AoI), a novel metric characterizing the data freshness of the system. This work studies the single-queue information update system and strengthens the seminal results of Sun et al. on the following fronts: (i) When designing the optimal offline schemes with full knowledge of the delay distributions, a new fixed-point-based method is proposed with quadratic convergence rate, an order-of-magnitude improvement over the state-of-the-art; (ii) When the distributional knowledge is unavailable (which is the norm in practice), two new low-complexity online algorithms are proposed, which provably attain the optimal average AoI penalty; and (iii) the online schemes also admit a modular architecture, which allows the designer to upgrade certain components to handle additional practical challenges. Two such upgrades are proposed for the situations: (iii.1) The AoI penalty function is also unknown and must be estimated on the fly, and (iii.2) the unknown delay distribution is Markovian instead of i.i.d. The performance of our schemes is either provably optimal or within 3% of the omniscient optimal offline solutions in all simulation scenarios. 

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2. Optimal Oblivious Routing With Concave Objectives for Structured Networks

   https://doi.org/10.1109/TNET.2023.3264632  

   Chitavisutthivong, Kanatip ; Supittayapornpong, Sucha ; Namyar, Pooria ; Zhang, Mingyang ; Yu, Minlan ; Govindan, Ramesh ( December 2023 , IEEE/ACM Transactions on Networking)
3. Scalable Binary Neural Network applications in Oblivious Inference

   https://doi.org/10.1145/3607192  

   Zhang, Xinqiao ; Samragh, Mohammad ; Hussain, Siam ; Huang, Ke ; Koushanfar, Farinaz ( July 2023 , ACM Transactions on Embedded Computing Systems)

   Binary neural network (BNN) delivers increased compute intensity and reduces memory/data requirements for computation. Scalable BNN enables inference in a limited time due to different constraints. This paper explores the application of Scalable BNN in oblivious inference, a service provided by a server to mistrusting clients. Using this service, a client can obtain the inference result on his/her data by a trained model held by the server without disclosing the data or learning the model parameters. Two contributions of this paper are: 1) we devise lightweight cryptographic protocols explicitly designed to exploit the unique characteristics of BNNs. 2) we present an advanced dynamic exploration of the runtime-accuracy tradeoff of scalable BNNs in a single-shot training process. While previous works trained multiple BNNs with different computational complexities (which is cumbersome due to the slow convergence of BNNs), we train a single BNN that can perform inference under various computational budgets. Compared to CryptFlow2, the state-of-the-art technique in the oblivious inference of non-binary DNNs, our approach reaches 3 × faster inference while keeping the same accuracy. Compared to XONN, the state-of-the-art technique in the oblivious inference of binary networks, we achieve 2 × to 12 × faster inference while obtaining higher accuracy. 

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4. Making Your Program Oblivious: A Comparative Study for Side-channel-Safe Confidential Computing

   https://doi.org/10.1109/CLOUD60044.2023.00040  

   Alam, A K ; Chen, Keke ( July 2023 , IEEE)
5. Lower Memory Oblivious (Tensor) Subspace Embeddings with Fewer Random Bits: Modewise Methods for Least Squares

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   Iwen, Mark A. ; Needell, Deanna ; Rebrova, Elizaveta ; Zare, Ali ( January 2021 , SIAM Journal on Matrix Analysis and Applications)