Over-the-Air Collaborative Inference with Feature Differential Privacy

• URL: http://arxiv.org/abs/2406.00256v1
• Date: Sat, 1 Jun 2024 01:39:44 GMT
• Title: Over-the-Air Collaborative Inference with Feature Differential Privacy
• Authors: Mohamed Seif, Yuqi Nie, Andrea Goldsmith, Vincent Poor,
• Abstract summary: Collaborative inference can enhance Artificial Intelligence (AI) applications, including autonomous driving, personal identification, and activity classification. The transmission of extracted features entails the potential risk of exposing sensitive personal data. New privacy-protecting collaborative inference mechanism is developed.
• Score: 8.099700053397278
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Collaborative inference in next-generation networks can enhance Artificial Intelligence (AI) applications, including autonomous driving, personal identification, and activity classification. This method involves a three-stage process: a) data acquisition through sensing, b) feature extraction, and c) feature encoding for transmission. Transmission of the extracted features entails the potential risk of exposing sensitive personal data. To address this issue, in this work a new privacy-protecting collaborative inference mechanism is developed. Under this mechanism, each edge device in the network protects the privacy of extracted features before transmitting them to a central server for inference. This mechanism aims to achieve two main objectives while ensuring effective inference performance: 1) reducing communication overhead, and 2) maintaining strict privacy guarantees during features transmission.

Related papers

• Facial Expression Recognition with Controlled Privacy Preservation and Feature Compensation [24.619279669211842]
  Facial expression recognition (FER) systems raise significant privacy concerns due to the potential exposure of sensitive identity information. This paper presents a study on removing identity information while preserving FER capabilities. We introduce a controlled privacy enhancement mechanism to optimize performance and a feature compensator to enhance task-relevant features without compromising privacy.
  arXiv  Detail & Related papers  (2024-11-29T23:12:38Z)
• Inference Privacy: Properties and Mechanisms [8.471466670802817]
  Inference Privacy (IP) can allow a user to interact with a model while providing a rigorous privacy guarantee for the users' data at inference. We present two types of mechanisms for achieving IP: namely, input perturbations and output perturbations which are customizable by the users.
  arXiv  Detail & Related papers  (2024-11-27T20:47:28Z)
• 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)
• Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification [54.1447806347273]
  Amplification by subsampling is one of the main primitives in machine learning with differential privacy. We propose the first general framework for deriving mechanism-specific guarantees. We analyze how subsampling affects the privacy of groups of multiple users.
  arXiv  Detail & Related papers  (2024-03-07T19:36:05Z)
• Diff-Privacy: Diffusion-based Face Privacy Protection [58.1021066224765]
  In this paper, we propose a novel face privacy protection method based on diffusion models, dubbed Diff-Privacy. Specifically, we train our proposed multi-scale image inversion module (MSI) to obtain a set of SDM format conditional embeddings of the original image. Based on the conditional embeddings, we design corresponding embedding scheduling strategies and construct different energy functions during the denoising process to achieve anonymization and visual identity information hiding.
  arXiv  Detail & Related papers  (2023-09-11T09:26:07Z)
• Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy [51.11280118806893]
  We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability. We study the local differential privacy guarantees of discrete-valued mechanisms with finite output space through the lens of $f$-differential privacy (DP) More specifically, we advance the existing literature by deriving tight $f$-DP guarantees for a variety of discrete-valued mechanisms.
  arXiv  Detail & Related papers  (2023-02-19T16:58:53Z)
• Graph-Homomorphic Perturbations for Private Decentralized Learning [64.26238893241322]
  Local exchange of estimates allows inference of data based on private data. perturbations chosen independently at every agent, resulting in a significant performance loss. We propose an alternative scheme, which constructs perturbations according to a particular nullspace condition, allowing them to be invisible.
  arXiv  Detail & Related papers  (2020-10-23T10:35:35Z)
• Bandwidth-Adaptive Feature Sharing for Cooperative LIDAR Object Detection [2.064612766965483]
  Situational awareness as a necessity in the connected and autonomous vehicles (CAV) domain. Cooperative mechanisms have provided a solution to improve situational awareness by utilizing high speed wireless vehicular networks. We propose a mechanism to add flexibility in adapting to communication channel capacity and a novel decentralized shared data alignment method.
  arXiv  Detail & Related papers  (2020-10-22T00:12:58Z)
• Differentially Private Multi-Agent Planning for Logistic-like Problems [70.3758644421664]
  This paper proposes a novel strong privacy-preserving planning approach for logistic-like problems. Two challenges are addressed: 1) simultaneously achieving strong privacy, completeness and efficiency, and 2) addressing communication constraints. To the best of our knowledge, this paper is the first to apply differential privacy to the field of multi-agent planning.
  arXiv  Detail & Related papers  (2020-08-16T03:43:09Z)

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.