Vectorized secure evaluation of decision forests

Malik, Raghav; Singhal, Vidush; Gottfried, Benjamin; Kulkarni, Milind

doi:10.1145/3453483.3454094

Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation 2021

DOI: 10.1145/3453483.3454094

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Vectorized secure evaluation of decision forests

Raghav Malik

Vidush Singhal

Benjamin Gottfried

et al.

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Cited by 4 publications

References 9 publications

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Taype: A Policy-Agnostic Language for Oblivious Computation

Delaware

2023

Proc. ACM Program. Lang.

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Secure multiparty computation (MPC) allows for joint computation over private data from multiple entities, usually backed by powerful cryptographic techniques that protect sensitive data. Several high-level programming languages have been proposed to make writing MPC applications accessible to non-experts. These languages typically require developers to enforce security policies within the logic of the secure application itself, making it difficult to update security requirements, or to experiment with different policies. This paper presents the design and implementation of Taype, a language that permits security concerns to be decoupled from the program logic. To do so, Taype provides the first implementation of oblivious algebraic data types and tape semantics, two language features recently proposed by a core calculus for oblivious computation, λ OADT + . We evaluate our implementation of Taype on a range of benchmarks, demonstrating its ability to encode a range of security polices for a rich class of data types.

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Taype: A Policy-Agnostic Language for Oblivious Computation

Delaware

2023

Proc. ACM Program. Lang.

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Optimizing Homomorphic Evaluation Circuits by Program Synthesis and Time-bounded Exhaustive Search

Lee

et al. 2023

ACM Trans. Program. Lang. Syst.

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We present a new and general method for optimizing homomorphic evaluation circuits. Although fully homomorphic encryption (FHE) holds the promise of enabling safe and secure third party computation, building FHE applications has been challenging due to their high computational costs. Domain-specific optimizations require a great deal of expertise on the underlying FHE schemes, and FHE compilers that aims to lower the hurdle, generate outcomes that are typically sub-optimal as they rely on manually-developed optimization rules. In this paper, based on the prior work of FHE compilers, we propose a method for automatically learning and using optimization rules for FHE circuits. Our method focuses on reducing the maximum multiplicative depth, the decisive performance bottleneck, of FHE circuits by combining program synthesis, term rewriting, and equality saturation. It first uses program synthesis to learn equivalences of small circuits as rewrite rules from a set of training circuits. Then, we perform term rewriting on the input circuit to obtain a new circuit that has lower multiplicative depth. Our rewriting method uses the equational matching with generalized version of the learned rules, and its soundness property is formally proven. Our optimizations also try to explore every possible alternative order of applying rewrite rules by time-bounded exhaustive search technique called equality saturation. Experimental results show that our method generates circuits that can be homomorphically evaluated 1.08x – 3.17x faster (with the geometric mean of 1.56x) than the state-of-the-art method. Our method is also orthogonal to existing domain-specific optimizations.

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Taypsi: Static Enforcement of Privacy Policies for Policy-Agnostic Oblivious Computation

Ye,

Delaware

2024

Proc. ACM Program. Lang.

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Secure multiparty computation (MPC) techniques enable multiple parties to compute joint functions over their private data without sharing that data with other parties, typically by employing powerful cryptographic protocols to protect individual's data. One challenge when writing such functions is that most MPC languages force users to intermix programmatic and privacy concerns in a single application, making it difficult to change or audit a program's underlying privacy policy. Prior policy-agnostic MPC languages relied on dynamic enforcement to decouple privacy requirements from program logic. Unfortunately, the resulting overhead makes it difficult to scale MPC applications that manipulate structured data. This work proposes to eliminate this overhead by instead transforming programs into semantically equivalent versions that statically enforce user-provided privacy policies. We have implemented this approach in a new MPC language, called Taypsi; our experimental evaluation demonstrates that the resulting system features considerable performance improvements on a variety of MPC applications involving structured data and complex privacy policies.

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Vectorized secure evaluation of decision forests

Cited by 4 publications

References 9 publications

Taype: A Policy-Agnostic Language for Oblivious Computation

Taype: A Policy-Agnostic Language for Oblivious Computation

Optimizing Homomorphic Evaluation Circuits by Program Synthesis and Time-bounded Exhaustive Search

Taypsi: Static Enforcement of Privacy Policies for Policy-Agnostic Oblivious Computation

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