Enhancing Noisy Functional Encryption for Privacy-Preserving Machine Learning

• URL: http://arxiv.org/abs/2505.05843v1
• Date: Fri, 09 May 2025 07:33:09 GMT
• Title: Enhancing Noisy Functional Encryption for Privacy-Preserving Machine Learning
• Authors: Linda Scheu-Hachtel, Jasmin Zalonis,
• Abstract summary: Functional encryption (FE) has attracted interest in privacy-preserving machine learning (PPML)<n>We extend the notion of noisy multi-input functional encryption (NMIFE) to (dynamic) noisy multi-client functional encryption ((Dy)NMCFE)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Functional encryption (FE) has recently attracted interest in privacy-preserving machine learning (PPML) for its unique ability to compute specific functions on encrypted data. A related line of work focuses on noisy FE, which ensures differential privacy in the output while keeping the data encrypted. We extend the notion of noisy multi-input functional encryption (NMIFE) to (dynamic) noisy multi-client functional encryption ((Dy)NMCFE), which allows for more flexibility in the number of data holders and analyses, while protecting the privacy of the data holder with fine-grained access through the usage of labels. Following our new definition of DyNMCFE, we present DyNo, a concrete inner-product DyNMCFE scheme. Our scheme captures all the functionalities previously introduced in noisy FE schemes, while being significantly more efficient in terms of space and runtime and fulfilling a stronger security notion by allowing the corruption of clients. To further prove the applicability of DyNMCFE, we present a protocol for PPML based on DyNo. According to this protocol, we train a privacy-preserving logistic regression.

Related papers

• HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption [3.0841649700901117]
  Fully Homomorphic Encryption (FHE) is an encryption scheme that not only encrypts data but also allows for computations to be applied directly on the encrypted data.<n>In this paper, we explore the challenges and opportunities when applying FHE to Deep Learning Recommendation Models (DLRM)<n>We develop novel methods for performing compressed embedding lookups in order to reduce FHE computational costs while keeping the underlying model performant.
  arXiv  Detail & Related papers  (2025-06-22T19:40:04Z)
• Urania: Differentially Private Insights into AI Use [104.7449031243196]
  $Urania$ provides end-to-end privacy protection by leveraging DP tools such as clustering, partition selection, and histogram-based summarization.<n>Results show the framework's ability to extract meaningful conversational insights while maintaining stringent user privacy.
  arXiv  Detail & Related papers  (2025-06-05T07:00:31Z)
• The Communication-Friendly Privacy-Preserving Machine Learning against Malicious Adversaries [14.232901861974819]
  Privacy-preserving machine learning (PPML) is an innovative approach that allows for secure data analysis while safeguarding sensitive information. We introduce efficient protocol for secure linear function evaluation. We extend the protocol to handle linear and non-linear layers, ensuring compatibility with a wide range of machine-learning models.
  arXiv  Detail & Related papers  (2024-11-14T08:55:14Z)
• Collaborative Inference over Wireless Channels with Feature Differential Privacy [57.68286389879283]
  Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications. transmitting extracted features poses a significant privacy risk, as sensitive personal data can be exposed during the process. We propose a novel privacy-preserving collaborative inference mechanism, wherein each edge device in the network secures the privacy of extracted features before transmitting them to a central server for inference.
  arXiv  Detail & Related papers  (2024-10-25T18:11:02Z)
• Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning [62.224804688233]
  differential privacy (DP) offers a promising solution by ensuring models are 'almost indistinguishable' with or without any particular privacy unit. We study user-level DP motivated by applications where it necessary to ensure uniform privacy protection across users.
  arXiv  Detail & Related papers  (2024-06-20T13:54:32Z)
• Privacy-Preserving Traceable Functional Encryption for Inner Product [0.3683202928838613]
  New primitive called traceable functional encryption for inner product (TFE-IP) has been proposed. Privacy protection of user's identities has not been considered in the existing TFE-IP schemes.
  arXiv  Detail & Related papers  (2024-04-07T08:09:46Z)
• THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption [112.02441503951297]
  Privacy-preserving inference of transformer models is on the demand of cloud service users. We introduce $textitTHE-X$, an approximation approach for transformers, which enables privacy-preserving inference of pre-trained models.
  arXiv  Detail & Related papers  (2022-06-01T03:49:18Z)
• SoK: Privacy Preserving Machine Learning using Functional Encryption: Opportunities and Challenges [1.2183405753834562]
  We focus on Inner-product-FE and Quadratic-FE-based machine learning models for the privacy-preserving machine learning (PPML) applications. To the best of our knowledge, this is the first work to systematize FE-based PPML approaches.
  arXiv  Detail & Related papers  (2022-04-11T14:15:36Z)
• 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)
• CryptoSPN: Privacy-preserving Sum-Product Network Inference [84.88362774693914]
  We present a framework for privacy-preserving inference of sum-product networks (SPNs) CryptoSPN achieves highly efficient and accurate inference in the order of seconds for medium-sized SPNs.
  arXiv  Detail & Related papers  (2020-02-03T14:49:18Z)

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.