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1. Private Analytics via Streaming, Sketching, and Silently Verifiable Proofs

   Rathee, Mayank; Zhang, Yuwen; Corrigan-Gibbs, Henry; Popa, Raluca Ada (May 2024, IEEE Security and Privacy 2024)

   We present Whisper, a system for privacy-preserving collection of aggregate statistics. Like prior systems, a Whisper deployment consists of a small set of non-colluding servers; these servers compute aggregate statistics over data from a large number of users without learning the data of any individual user. Whisper's main contribution is that its server-to-server communication cost and its server-side storage costs scale sublinearly with the total number of users. In particular, prior systems required the servers to exchange a few bits of information to verify the well-formedness of each client submission. In contrast, Whisper uses silently verifiable proofs, a new type of proof system on secret-shared data that allows the servers to verify an arbitrarily large batch of proofs by exchanging a single 128-bit string. This improvement comes with increased client-to-server communication, which, in cloud computing, is typically cheaper (or even free) than the cost of egress for server-to-server communication. To reduce server storage, Whisper approximates certain statistics using small-space sketching data structures. Applying randomized sketches in an environment with adversarial clients requires a careful and novel security analysis. In a deployment with two servers and 100,000 clients of which 1% are malicious, Whisper can improve server-to-server communication for vector sum by three orders of magnitude while each client's communication increases by only 10%. 

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2. ARISE: High-Capacity AR Offloading Inference Serving via Proactive Scheduling

   https://doi.org/10.1145/3643832.3661894  

   Kong, Z Jonny; Xu, Qiang; Hu, Y Charlie (June 2024, ACM)

   With faster wireless networks and server GPUs, offloading high-accuracy but compute-intensive AR tasks implemented in Deep Neural Networks (DNNs) to edge servers offers a promising way to support high-QoE Augmented/Mixed Reality (AR/MR) applications. A cost-effective way for AR app vendors to deploy such edge-assisted AR apps to support a large user base is to use commercial Machine-Learning-as-a-Service (MLaaS) deployed at the edge cloud. To maximize cost-effectiveness, such an MLaaS provider faces a key design challenge, \ie how to maximize the number of clients concurrently served by each GPU server in its cluster while meeting per-client AR task accuracy SLAs. The above AR offloading inference serving problem differs from generic inference serving or video analytics serving in one fundamental way: due to the use of local tracking which reuses the last server-returned inference result to derive results for the current frame, the offloading frequency and end-to-end latency of each AR client directly affect its AR task accuracy (for all the frames). In this paper, we present ARISE, a framework that optimizes the edge server capacity in serving edge-assisted AR clients. Our design exploits the intricate interplay between per-client offloading schedule and batched inference on the server via proactively coordinating offloading request streams from different AR clients. Our evaluation using a large set of emulated AR clients and a 10-phone testbed shows that \name supports 1.7x--6.9x more clients compared to various baselines while keeping the per-client accuracy within the client-specified accuracy SLAs. 

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3. All-Norm Load Balancing in Graph Streams via the Multiplicative Weights Update Method

   https://doi.org/10.4230/LIPIcs.ITCS.2023.7  

   Assadi, Sepehr; Bernstein, Aaron; Langley, Zachary (January 2023, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Tauman_Kalai, Yael (Ed.)

   In the weighted load balancing problem, the input is an n-vertex bipartite graph between a set of clients and a set of servers, and each client comes with some nonnegative real weight. The output is an assignment that maps each client to one of its adjacent servers, and the load of a server is then the sum of the weights of the clients assigned to it. The goal is to find an assignment that is well-balanced, typically captured by (approximately) minimizing either the 𝓁_∞- or 𝓁₂-norm of the server loads. Generalizing both of these objectives, the all-norm load balancing problem asks for an assignment that approximately minimizes all 𝓁_p-norm objectives for p ≥ 1, including p = ∞, simultaneously. Our main result is a deterministic O(log n)-pass O(1)-approximation semi-streaming algorithm for the all-norm load balancing problem. Prior to our work, only an O(log n)-pass O(log n)-approximation algorithm for the 𝓁_∞-norm objective was known in the semi-streaming setting. Our algorithm uses a novel application of the multiplicative weights update method to a mixed covering/packing convex program for the all-norm load balancing problem involving an infinite number of constraints. 

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4. PLASMA: Private, Lightweight Aggregated Statistics against Malicious Adversaries

   https://doi.org/10.56553/popets-2024-0064  

   Mouris, Dimitris; Sarkar, Pratik; Tsoutsos, Nektarios Georgios (July 2024, Proceedings on Privacy Enhancing Technologies)

   Sherr, Micah; Shafiq, Zubair (Ed.)

   Private heavy-hitters is a data-collection task where multiple clients possess private bit strings, and data-collection servers aim to identify the most popular strings without learning anything about the clients' inputs. In this work, we introduce PLASMA: a private analytics framework in the three-server setting that protects the privacy of honest clients and the correctness of the protocol against a coalition of malicious clients and a malicious server. Our core primitives are a verifiable incremental distributed point function (VIDPF) and a batched consistency check, which are of independent interest. Our VIDPF introduces new methods to validate client inputs based on hashing. Meanwhile, our batched consistency check uses Merkle trees to validate multiple client sessions together in a batch. This drastically reduces server communication across multiple client sessions, resulting in significantly less communication compared to related works. Finally, we compare PLASMA with the recent works of Asharov et al. (CCS'22) and Poplar (S&P'21) and compare in terms of monetary cost for different input sizes. 

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5. A Faster Combinatorial Algorithm for Maximum Bipartite Matching

   Chuzhoy, Julia; Khanna, Sanjeev (January 2024, Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA))

   The maximum bipartite matching problem is among the most fundamental and well-studied problems in combinatorial optimization. A beautiful and celebrated combinatorial algorithm of Hopcroft and Karp [26] shows that maximum bipartite matching can be solved in O(m√n) time on a graph with n vertices and m edges. For the case of very dense graphs, a different approach based on fast matrix multiplication was subsequently developed [27, 39], that achieves a running time of O(n2.371). For the next several decades, these results represented the fastest known algorithms for the problem until in 2013, a ground-breaking work of Madry [36] gave a significantly faster algorithm for sparse graphs. Subsequently, a sequence of works developed increasingly faster algorithms for solving maximum bipartite matching, and more generally directed maximum flow, culminating in a spectacular recent breakthrough [9] that gives an m1+o(1) time algorithm for maximum bipartite matching (and more generally, for min cost flows). These more recent developments collectively represented a departure from earlier combinatorial approaches: they all utilized continuous techniques based on interior-point methods for solving linear programs. This raises a natural question: are continuous techniques essential to obtaining fast algorithms for the bipartite matching problem? Our work makes progress on this question by presenting a new, purely combinatorial algorithm for bipartite matching, that, on moderately dense graphs outperforms both Hopcroft- Karp and the fast matrix multiplication based algorithms. Similar to the classical algorithms for bipartite matching, our approach is based on iteratively augmenting a current matching using augmenting paths in the (directed) residual flow network. A common method for designing fast algorithms for directed flow problems is via the multiplicative weights update (MWU) framework, that effectively reduces the flow problem to decremental single-source shortest paths (SSSP) in directed graphs. Our main observation is that a slight modification of this reduction results in a special case of SSSP that appears significantly easier than general decremental directed SSSP. Our main technical contribution is an efficient algorithm for this special case of SSSP, that outperforms the current state of the art algorithms for general decremental SSSP with adaptive adversary, leading to a deterministic algorithm for bipartite matching, whose running time is Õ(m1/3n5/3). This new algorithm thus starts to outperform the Hopcroft-Karp algorithm in graphs with m = ω(n7/4), and it also outperforms the fast matrix multiplication based algorithms on dense graphs. We believe that this framework for obtaining faster combinatorial algorithms for bipartite matching by exploiting the special properties of the resulting decremental SSSP instances is one of the main conceptual contributions of our work that we hope paves the way for even faster combinatorial algorithms for bipartite matching. Finally, using a standard reduction from the maximum vertex-capacitated s-t flow problem in directed graphs to maximum bipartite matching, we also obtain an O(m1/3n5/3) time deterministic algorithm for maximum vertex-capacitated s-t flow when all vertex capacities are identical. 

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