TERMinator Suite: Benchmarking Privacy-Preserving Architectures

Mouris, Dimitris; Tsoutsos, Nektarios Georgios; Maniatakos, Michail

doi:10.1109/lca.2018.2812814

Cited by 11 publications

(2 citation statements)

References 13 publications

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“…For comparison on general-purpose computation, we use a set of data-oblivious benchmarks from the TERMinator Suite [39] that are grouped into three categories: 1) Basic -heavily based on arithmetic and logical operations: Bubble Sort (bsort), Insertion Sort (isort), Matrix Multiplication (matrix), and Sieve of Erastothenes (sieve); 2) Encoder -implementing bitwise-intensive cryptographic and hash applications: Jenkins (jen) and Speck Cipher (speck); and 3) Microbenchmarks: the addition-intensive Fibonnaci (fib) and the multiplication-intensive Factorial (fact). Table 2 presents the execution time (in seconds) for the benchmarks running on 8-bit and 32-bit SecureInt variables with λ = 80 against TFHE.…”

Section: ) Tfhementioning

confidence: 99%

Practical Data-in-Use Protection Using Binary Decision Diagrams

et al. 2020

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Protection of data-in-use, contrary to the protection of data-at-rest or data-in-transit, remains a challenge. Cryptography advances such as Fully Homomorphic Encryption (FHE) provide theoretical, albeit impractical, solutions to functionally-complete computation over encrypted operands, necessary for general-purpose computation. In this work, we propose a practical data-in-use protection mechanism that, contrary to application-specific homomorphic encryption approaches, focuses on arbitrary computation native to established programming languages, such as C++. Therefore, our work provides a more efficient alternative to FHE schemes that can be used for general-purpose computation. Specifically, we use Binary Decision Diagrams (BDD) to transform high-level programming operations to their equivalents working on protected data. To automate this, we develop a framework that allows automatic conversion of program expressions over encrypted operands into efficient circuits that are reduced using BDDs and can simulate corresponding composed operations. Our experimental results show that our methodology is orders of magnitude faster than state-of-the-art FHE schemes and enables execution of real C++ applications with practical overheads. Our framework is complemented with security analysis proving resistance to different attack methods.

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Section: ) Tfhementioning

confidence: 99%

Practical Data-in-Use Protection Using Binary Decision Diagrams

et al. 2020

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“…For the former, modern FHE schemes offer one or more mechanisms to manage this noise, with each technique having different capabilities in terms of noise reduction as well as computational overhead. For the latter, if a loop termination condition remains encrypted, a server executing an encrypted program may not be able to decide if or when the execution ends (i.e., there exists a "termination problem" [17,22,65,80,88]).…”

Section: Introductionmentioning

confidence: 99%

SoK: New Insights into Fully Homomorphic Encryption Libraries via Standardized Benchmarks

Gouert

Mouris

Tsoutsos

2023

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Fully homomorphic encryption (FHE) enables arbitrary computation on encrypted data, allowing users to upload ciphertexts to cloud servers for computation while mitigating privacy risks. Many cryptographic schemes fall under the umbrella of FHE, and each scheme has several open-source implementations with its own strengths and weaknesses. Nevertheless, developers have no straightforward way to choose which FHE scheme and implementation is best suited for their application needs, especially considering that each scheme offers different security, performance, and usability guarantees. To allow programmers to effectively utilize the power of FHE, we employ a series of benchmarks called the Terminator 2 Benchmark Suite and present new insights gained from running these algorithms with a variety of FHE back - ends. Contrary to generic benchmarks that do not take into consideration the inherent challenges of encrypted computation, our methodology is tailored to the secure computational primitives of each target FHE implementation. To ensure fair comparisons, we developed a versatile compiler(called T2) that converts arbitrary benchmarks written in a domain - specific language into identical encrypted programs running on different popular FHE libraries as a backend.Our analysis exposes for the first time the advantages and disadvantages of each FHE library as well as the types of applications most suited for each computational domain(i.e., binary, integer, and floating - point).

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