Blind Evaluation Framework for Fully Homomorphic Encryption and Privacy-Preserving Machine Learning

• URL: http://arxiv.org/abs/2310.13140v4
• Date: Tue, 27 Aug 2024 22:49:02 GMT
• Title: Blind Evaluation Framework for Fully Homomorphic Encryption and Privacy-Preserving Machine Learning
• Authors: Hunjae "Timothy" Lee, Corey Clark,
• Abstract summary: Blind Evaluation Framework (BEF) is a cryptographically secure programming framework. It enables blind, but correct, execution of programming logic without Interactive Rounds of Decryption and Evaluation (IRDE) This is the first framework to enable both training and inference of machine learning models with FHE without decryption rounds.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the domain of Privacy-Preserving Machine Learning (PPML), Fully Homomorphic Encryption (FHE) is often used for encrypted computation to allow secure and privacy-preserving outsourcing of machine learning modeling. While FHE enables encrypted arithmetic operations, execution of programmatic logic such as control structures or conditional programming have remained a challenge. As a result, progress in encrypted training of PPML with FHE has been relatively stagnant compared to encrypted inference owing to the considerably higher logical complexity required in training. In addition, prior works that have demonstrated encrypted training use Interactive Rounds of Decryption and Evaluation (IRDE), where certain operations are decrypted and evaluated in plaintext using interactive rounds between the untrusted computing party (server) and the trusted private-key owner (client). In decision tree training for example, the current state-of-the-art requires d-rounds of IRDE for tree-depth of d. To address this issue in PPML and FHE, we introduce the Blind Evaluation Framework (BEF), a cryptographically secure programming framework that enables blind, but correct, execution of programming logic without IRDE. This is achieved by deconstructing programming logic into binary circuits and binary arithmetic to find alternative representations of logical statements, and adopting them to FHE for secure logical programming. To the best of our knowledge, this is the first framework to enable both training and inference of PPML models with FHE without decryption rounds. By advancing the state-of-the-art in IRDE efficiency by eliminating IRDE entirely, BEF enables adoption of FHE in use-cases where large amounts of computing services are available without the ability to have trusted clients available to perform decryption rounds.

Related papers

• Secure numerical simulations using fully homomorphic encryption [2.923600136516929]
  homomorphic encryption (FHE) allows for secure computations on encrypted data. We investigate the viability of using FHE for privacy-preserving numerical simulations of partial differential equations.
  arXiv  Detail & Related papers  (2024-10-29T07:47:10Z)
• Encrypted system identification as-a-service via reliable encrypted matrix inversion [0.0]
  Encrypted computation opens up promising avenues across a plethora of application domains. In particular, Arithmetic homomorphic encryption is a natural fit for cloud-based computational services. This paper presents an encrypted system identification service enabled by a reliable encrypted solution to at least squares problems.
  arXiv  Detail & Related papers  (2024-10-27T20:00:04Z)
• Encryption-Friendly LLM Architecture [11.386436468650016]
  Homomorphic encryption (HE) is a cryptographic protocol supporting arithmetic computations in encrypted states. We propose a modified HE-friendly transformer architecture with an emphasis on inference following personalized (private) fine-tuning.
  arXiv  Detail & Related papers  (2024-10-03T13:48:35Z)
• FoC: Figure out the Cryptographic Functions in Stripped Binaries with LLMs [54.27040631527217]
  We propose a novel framework called FoC to Figure out the Cryptographic functions in stripped binaries. FoC-BinLLM outperforms ChatGPT by 14.61% on the ROUGE-L score. FoC-Sim outperforms the previous best methods with a 52% higher Recall@1.
  arXiv  Detail & Related papers  (2024-03-27T09:45:33Z)
• Mathematical Algorithm Design for Deep Learning under Societal and Judicial Constraints: The Algorithmic Transparency Requirement [65.26723285209853]
  We derive a framework to analyze whether a transparent implementation in a computing model is feasible. Based on previous results, we find that Blum-Shub-Smale Machines have the potential to establish trustworthy solvers for inverse problems.
  arXiv  Detail & Related papers  (2024-01-18T15:32:38Z)
• When Do Program-of-Thoughts Work for Reasoning? [51.2699797837818]
  We propose complexity-impacted reasoning score (CIRS) to measure correlation between code and reasoning abilities. Specifically, we use the abstract syntax tree to encode the structural information and calculate logical complexity. Code will be integrated into the EasyInstruct framework at https://github.com/zjunlp/EasyInstruct.
  arXiv  Detail & Related papers  (2023-08-29T17:22:39Z)
• Reinforcement Learning on Encrypted Data [58.39270571778521]
  We present a preliminary, experimental study of how a DQN agent trained on encrypted states performs in environments with discrete and continuous state spaces. Our results highlight that the agent is still capable of learning in small state spaces even in presence of non-deterministic encryption, but performance collapses in more complex environments.
  arXiv  Detail & Related papers  (2021-09-16T21:59:37Z)
• How could Neural Networks understand Programs? [67.4217527949013]
  It is difficult to build a model to better understand programs, by either directly applying off-the-shelf NLP pre-training techniques to the source code, or adding features to the model by theshelf. We propose a novel program semantics learning paradigm, that the model should learn from information composed of (1) the representations which align well with the fundamental operations in operational semantics, and (2) the information of environment transition.
  arXiv  Detail & Related papers  (2021-05-10T12:21:42Z)
• Efficient CNN Building Blocks for Encrypted Data [6.955451042536852]
  Homomorphic Encryption (FHE) is a promising technique to enable machine learning and inferencing. We show that operational parameters of the chosen FHE scheme have a major impact on the design of the machine learning model. Our empirical study shows that choice of aforementioned design parameters result in significant trade-offs between accuracy, security level, and computational time.
  arXiv  Detail & Related papers  (2021-01-30T21:47:23Z)
• Cryptotree: fast and accurate predictions on encrypted structured data [0.0]
  Homomorphic Encryption (HE) is acknowledged for its ability to allow computation on encrypted data, where both the input and output are encrypted. We propose Cryptotree, a framework that enables the use of Random Forests (RF), a very powerful learning procedure compared to linear regression.
  arXiv  Detail & Related papers  (2020-06-15T11:48:01Z)
• Can We Learn Heuristics For Graphical Model Inference Using Reinforcement Learning? [114.24881214319048]
  We show that we can learn programs, i.e., policies, for solving inference in higher order Conditional Random Fields (CRFs) using reinforcement learning. Our method solves inference tasks efficiently without imposing any constraints on the form of the potentials.
  arXiv  Detail & Related papers  (2020-04-27T19:24:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.