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WAHC '24: Proceedings of the 12th Workshop on Encrypted Computing & Applied Homomorphic Cryptography
ACM2024 Proceeding
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
Conference:
CCS '24: ACM SIGSAC Conference on Computer and Communications Security Salt Lake City UT USA October 14 - 18, 2024
ISBN:
979-8-4007-1241-8
Published:
19 November 2024
Sponsors:
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Abstract

It is our great pleasure to welcome you to the 12th annual Workshop on Encrypted Computing and Applied Homomorphic Cryptography - WAHC'24. WAHC was created in 2013 as a forum to organize and foster discussion of a wide variety of aspects of encrypted computing and secure computation.

In a world where distance is no longer an obstacle for cooperation, secure computation is becoming a key feature of current and future information systems. Distributed network applications and cloud architectures are at danger because lots of personal consumer data is aggregated in all kinds of formats and for various purposes. Industry and consumer electronics companies are facing massive threats like theft of intellectual property and industrial espionage. Public infrastructure has to be secured against sabotage and manipulation. A possible solution is encrypted computing: Data can be processed on remote, possibly insecure resources, while program code and data is encrypted all the time. This allows to outsource the computation of confidential information independently from the trustworthiness or the security level of the remote system. The goal of the workshop is to bring together researchers with practitioners and industry to present, discuss and to share the latest progress in the field. We want to exchange ideas that address real-world problems with practical approaches and solutions.

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SESSION: Session I
research-article
Time-Memory Trade-off Algorithms for Homomorphically Evaluating Look-up Table in TFHE

We propose time-memory trade-off algorithms for evaluating look-up table (LUT) in both the leveled homomorphic encryption (LHE) and fully homomorphic encryption (FHE) modes in TFHE. For an arbitrary n-bit Boolean function, we reduce evaluation time by a ...

research-article
vFHE: Verifiable Fully Homomorphic Encryption

Fully Homomorphic Encryption (FHE) is a powerful building block for secure and private applications. However, state-of-the-art FHE schemes do not offer any integrity guarantees, which can lead to devastating correctness and security issues when FHE is ...

research-article
HEBridge: Connecting Arithmetic and Logic Operations in FV-style HE Schemes

Fully homomorphic encryption (FHE) allows computation over encrypted data without decryption and is considered one of the most essential primitives for privacy-preserving applications. However, there are still no universal FHE schemes that can support ...

SESSION: Keynote talk
short-paper
Security and Performance-Aware Cloud Computing with Homomorphic Encryption and Trusted Execution Environment

In recent years, cloud computing has been widely adopted due to its high scalability and low development and operational costs; however, privacy and intellectual property concerns arise when cloud servers handle user data and programs. Homomorphic ...

SESSION: Session II
short-paper
Open Access
Oraqle: A Depth-Aware Secure Computation Compiler

In the past decade, tens of homomorphic encryption compilers have been released, and there are good reasons for these compilers to exist. Firstly, homomorphic encryption is a powerful secure computation technique in that it is relatively easy for parties ...

research-article
On the Synthesis of High-performance Homomorphic Boolean Circuits

The rapid growth of cloud computing has intensified the need for secure data outsourcing solutions. Fully homomorphic encryption (FHE) offers a promising approach by enabling computations on encrypted data without exposing the plaintext. This paper ...

research-article
Open Access
Training Encrypted Neural Networks on Encrypted Data with Fully Homomorphic Encryption

Training machine and deep learning models on encrypted data is the next challenge in the field of privacy-preserving Machine and Deep Learning. The related literature in this field is very limited, since most of the solutions focus only on inference on ...

SESSION: Session III
research-article
Open Access
Faster Homomorphic Evaluation of Arbitrary Bivariate Integer Functions via Homomorphic Linear Transformation

Fully homomorphic encryption (FHE) can perform computations on encrypted data, allowing us to analyze sensitive data while maintaining security. Several popular FHE schemes, such as BGV and BFV, are suitable for arithmetic circuits. However, there is ...

Contributors
  • Intel Corporation
  • Norwegian University of Science and Technology
  • New Jersey Institute of Technology
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Acceptance Rates

Overall Acceptance Rate 6 of 17 submissions, 35%
YearSubmittedAcceptedRate
WAHC '1817635%
Overall17635%