190 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Dynamic Dictionary with Subconstant Wasted Bits per Key

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    Dictionaries have been one of the central questions in data structures. A dictionary data structure maintains a set of key-value pairs under insertions and deletions such that given a query key, the data structure efficiently returns its value. The state-of-the-art dictionaries [Bender, Farach-Colton, Kuszmaul, Kuszmaul, Liu 2022] store nn key-value pairs with only O(nlog⁥(k)n)O(n \log^{(k)} n) bits of redundancy, and support all operations in O(k)O(k) time, for k≀log⁡∗nk \leq \log^* n. It was recently shown to be optimal [Li, Liang, Yu, Zhou 2023b]. In this paper, we study the regime where the redundant bits is R=o(n)R=o(n), and show that when RR is at least n/polylog⁥nn/\text{poly}\log n, all operations can be supported in O(log⁡∗n+log⁥(n/R))O(\log^* n + \log (n/R)) time, matching the lower bound in this regime [Li, Liang, Yu, Zhou 2023b]. We present two data structures based on which range RR is in. The data structure for R<n/log⁥0.1nR<n/\log^{0.1} n utilizes a generalization of adapters studied in [Berger, Kuszmaul, Polak, Tidor, Wein 2022] and [Li, Liang, Yu, Zhou 2023a]. The data structure for R≄n/log⁥0.1nR \geq n/\log^{0.1} n is based on recursively hashing into buckets with logarithmic sizes.Comment: 46 pages; SODA 202

    A Survey on Malware Detection with Graph Representation Learning

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    Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, they suffer from poor generalization to unknown attacks and can be easily circumvented using obfuscation techniques. In recent years, Machine Learning (ML) and notably Deep Learning (DL) achieved impressive results in malware detection by learning useful representations from data and have become a solution preferred over traditional methods. More recently, the application of such techniques on graph-structured data has achieved state-of-the-art performance in various domains and demonstrates promising results in learning more robust representations from malware. Yet, no literature review focusing on graph-based deep learning for malware detection exists. In this survey, we provide an in-depth literature review to summarize and unify existing works under the common approaches and architectures. We notably demonstrate that Graph Neural Networks (GNNs) reach competitive results in learning robust embeddings from malware represented as expressive graph structures, leading to an efficient detection by downstream classifiers. This paper also reviews adversarial attacks that are utilized to fool graph-based detection methods. Challenges and future research directions are discussed at the end of the paper.Comment: Preprint, submitted to ACM Computing Surveys on March 2023. For any suggestions or improvements, please contact me directly by e-mai

    Unbalanced Private Set Intersection from Homomorphic Encryption and Nested Cuckoo Hashing

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    Private Set Intersection (PSI) is a well-studied secure two-party computation problem in which a client and a server want to compute the intersection of their input sets without revealing additional information to the other party. With this work, we present nested Cuckoo hashing, a novel hashing approach that can be combined with additively homomorphic encryption (AHE) to construct an efficient PSI protocol for unbalanced input sets. We formally prove the security of our protocol against semi-honest adversaries in the standard model. Our protocol yields client computation and communication complexity that is sublinear in the server’s set size and is thus of interest to clients with limited resources. The implementation and empirical evaluation of our protocol using the exponential ElGamal and BGV/BFV encryption schemes attests to state-of-the-art practical performance

    Utopia: Fast and Efficient Address Translation via Hybrid Restrictive & Flexible Virtual-to-Physical Address Mappings

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    Conventional virtual memory (VM) frameworks enable a virtual address to flexibly map to any physical address. This flexibility necessitates large data structures to store virtual-to-physical mappings, which leads to high address translation latency and large translation-induced interference in the memory hierarchy. On the other hand, restricting the address mapping so that a virtual address can only map to a specific set of physical addresses can significantly reduce address translation overheads by using compact and efficient translation structures. However, restricting the address mapping flexibility across the entire main memory severely limits data sharing across different processes and increases data accesses to the swap space of the storage device, even in the presence of free memory. We propose Utopia, a new hybrid virtual-to-physical address mapping scheme that allows both flexible and restrictive hash-based address mapping schemes to harmoniously co-exist in the system. The key idea of Utopia is to manage physical memory using two types of physical memory segments: restrictive and flexible segments. A restrictive segment uses a restrictive, hash-based address mapping scheme that maps virtual addresses to only a specific set of physical addresses and enables faster address translation using compact translation structures. A flexible segment employs the conventional fully-flexible address mapping scheme. By mapping data to a restrictive segment, Utopia enables faster address translation with lower translation-induced interference. Utopia improves performance by 24% in a single-core system over the baseline system, whereas the best prior state-of-the-art contiguity-aware translation scheme improves performance by 13%.Comment: To appear in 56th IEEE/ACM International Symposium on Microarchitecture (MICRO), 202

    Applications

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    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

    LIPIcs, Volume 274, ESA 2023, Complete Volume

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    LIPIcs, Volume 274, ESA 2023, Complete Volum

    Panacea: Non-interactive and Stateless Oblivious RAM

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    Oblivious RAM (ORAM) allows a client to outsource storage to a remote server while hiding the data access pattern from the server. Many ORAM designs have been proposed to reduce the computational overhead and bandwidth blowup for the client. A recent work, Onion Ring ORAM (CCS\u2719), is able to achieve O(1)O(1) bandwidth blowup in the online phase using fully homomorphic encryption (FHE) techniques, at the cost of a computationally expensive client-side offline phase. Furthermore, such a scheme can be categorized as a stateful construction, meaning that the client has to locally maintain a dynamic state representing the order of remote database elements. We present Panacea: a novel design of ORAM based on FHE techniques, that is non-interactive and stateless, achieves O(1)O(1) bandwidth blowup, and does not require an expensive offline phase for the client to perform; in that sense, our design is the first of its kind among other ORAM designs. To provide the client with such performance benefits, our design delegates all expensive computation to the resourceful server. We additionally show how to boost the server performance significantly using probabilistic batch codes at the cost of only 1.5x in additional bandwidth blowup and 3x expansion in server storage, but less amortized bandwidth. Our experimental results show that our design, with the batching technique, is practical in terms of server computation overhead as well. Specifically, for a database size of 2192^{19}, it takes only 1.161.16 seconds of amortized computation time for a server to respond to a query. As a result of the statelessness and low computational overhead on the client, and reasonable computational overhead on the server, our design is very suitable to be deployed as a cloud-based privacy-preserving storage outsourcing solution with a portable client running on a lightweight device

    Cuckoo Hashing in Cryptography: Optimal Parameters, Robustness and Applications

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    Cuckoo hashing is a powerful primitive that enables storing items using small space with efficient querying. At a high level, cuckoo hashing maps nn items into bb entries storing at most ℓ\ell items such that each item is placed into one of kk randomly chosen entries. Additionally, there is an overflow stash that can store at most ss items. Many cryptographic primitives rely upon cuckoo hashing to privately embed and query data where it is integral to ensure small failure probability when constructing cuckoo hashing tables as it directly relates to the privacy guarantees. As our main result, we present a more query-efficient cuckoo hashing construction using more hash functions. For construction failure probability Ï”\epsilon, the query overhead of our scheme is O(1+log⁥(1/Ï”)/log⁥n)O(1 + \sqrt{\log(1/\epsilon)/\log n}). Our scheme has quadratically smaller query overhead than prior works for any target failure probability Ï”\epsilon. We also prove lower bounds matching our construction. Our improvements come from a new understanding of the locality of cuckoo hashing failures for small sets of items. We also initiate the study of robust cuckoo hashing where the input set may be chosen with knowledge of the hash functions. We present a cuckoo hashing scheme using more hash functions with query overhead O~(log⁥λ)\tilde{O}(\log \lambda) that is robust against poly(λ)(\lambda) adversaries. Furthermore, we present lower bounds showing that this construction is tight and that extending previous approaches of large stashes or entries cannot obtain robustness except with Ω(n)\Omega(n) query overhead. As applications of our results, we obtain improved constructions for batch codes and PIR. In particular, we present the most efficient explicit batch code and blackbox reduction from single-query PIR to batch PIR
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