Generative AI for Secure and Privacy-Preserving Mobile Crowdsensing

• URL: http://arxiv.org/abs/2405.10521v1
• Date: Fri, 17 May 2024 04:00:58 GMT
• Title: Generative AI for Secure and Privacy-Preserving Mobile Crowdsensing
• Authors: Yaoqi Yang, Bangning Zhang, Daoxing Guo, Hongyang Du, Zehui Xiong, Dusit Niyato, Zhu Han,
• Abstract summary: generative AI has attracted much attention from both academic and industrial fields. Secure and privacy-preserving mobile crowdsensing (SPPMCS) has been widely applied in data collection/ acquirement.
• Score: 74.58071278710896
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, generative AI has attracted much attention from both academic and industrial fields, which has shown its potential, especially in the data generation and synthesis aspects. Simultaneously, secure and privacy-preserving mobile crowdsensing (SPPMCS) has been widely applied in data collection/ acquirement due to an advantage on low deployment cost, flexible implementation, and high adaptability. Since generative AI can generate new synthetic data to replace the original data to be analyzed and processed, it can lower data attacks and privacy leakage risks for the original data. Therefore, integrating generative AI into SPPMCS is feasible and significant. Moreover, this paper investigates an integration of generative AI in SPPMCS, where we present potential research focuses, solutions, and case studies. Specifically, we firstly review the preliminaries for generative AI and SPPMCS, where their integration potential is presented. Then, we discuss research issues and solutions for generative AI-enabled SPPMCS, including security defense of malicious data injection, illegal authorization, malicious spectrum manipulation at the physical layer, and privacy protection on sensing data content, sensing terminals' identification and location. Next, we propose a framework for sensing data content protection with generative AI, and simulations results have clearly demonstrated the effectiveness of the proposed framework. Finally, we present major research directions for generative AI-enabled SPPMCS.

Related papers

• Sell Data to AI Algorithms Without Revealing It: Secure Data Valuation and Sharing via Homomorphic Encryption [10.12846924939717]
  We introduce the Trustworthy Influence Protocol (TIP), a privacy-preserving framework that enables buyers to quantify the utility of external data without decrypting the raw assets.<n>By integrating Homomorphic Encryption with gradient-based influence functions, our approach allows for the precise, blinded scoring of data points against a buyer's specific AI model.<n> Empirical simulations in healthcare and generative AI domains validate the framework's economic potential.
  arXiv  Detail & Related papers  (2025-12-04T16:35:09Z)
• How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy [52.00934156883483]
  Differential Privacy (DP) is a framework for reasoning about and limiting information leakage.<n>Differentially Private Synthetic data refers to synthetic data that preserves the overall trends of source data.
  arXiv  Detail & Related papers  (2025-12-02T21:14:39Z)
• What's the next frontier for Data-centric AI? Data Savvy Agents [71.76058707995398]
  We argue that data-savvy capabilities should be a top priority in the design of agentic systems.<n>We propose four key capabilities to realize this vision: Proactive data acquisition, Sophisticated data processing, Interactive test data synthesis, and Continual adaptation.
  arXiv  Detail & Related papers  (2025-11-02T17:09:29Z)
• Adversary-Aware Private Inference over Wireless Channels [51.93574339176914]
  AI-based sensing at wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications.<n>As sensitive personal data can be reconstructed by an adversary, transformation of the features are required to reduce the risk of privacy violations.<n>We propose a novel framework for privacy-preserving AI-based sensing, where devices apply transformations of extracted features before transmission to a model server.
  arXiv  Detail & Related papers  (2025-10-23T13:02:14Z)
• Processing of synthetic data in AI development for healthcare and the definition of personal data in EU law [0.0]
  Artificial intelligence (AI) has potential to transform healthcare, but it requires access to health data.<n>The study investigates whether synthetic data should be classified as personal data under the study.<n>The findings suggest synthetic data is likely anonymous, depending on certain factors, but highlights uncertainties about what constitutes reasonably likely risk.
  arXiv  Detail & Related papers  (2025-08-11T17:59:06Z)
• Rethinking Data Protection in the (Generative) Artificial Intelligence Era [115.71019708491386]
  We propose a four-level taxonomy that captures the diverse protection needs arising in modern (generative) AI models and systems.<n>Our framework offers a structured understanding of the trade-offs between data utility and control, spanning the entire AI pipeline.
  arXiv  Detail & Related papers  (2025-07-03T02:45:51Z)
• Privacy Preservation in Gen AI Applications [0.0]
  Large Language Models (LLMs) may unintentionally absorb and reveal Personally Identifiable Information (PII) from user interactions. Deep neural networks' intricacy makes it difficult to track down or stop the inadvertent storing and release of private information. This study tackles these issues by detecting Generative AI weaknesses through attacks such as data extraction, model inversion, and membership inference. It ensures privacy without sacrificing functionality by using methods to identify, alter, or remove PII before to dealing with LLMs.
  arXiv  Detail & Related papers  (2025-04-12T06:19:37Z)
• Evidencing Unauthorized Training Data from AI Generated Content using Information Isotopes [0.0]
  In a rush to stay competitive, some institutions may inadvertently or even deliberately include unauthorized data for AI training. We introduce the concept of information isotopes and elucidate their properties in tracing training data within opaque AI systems. We propose an information isotope tracing method designed to identify and provide evidence of unauthorized data usage.
  arXiv  Detail & Related papers  (2025-03-24T07:35:59Z)
• SafeSynthDP: Leveraging Large Language Models for Privacy-Preserving Synthetic Data Generation Using Differential Privacy [0.0]
  We investigate capability of Large Language Models (Ms) to generate synthetic datasets with Differential Privacy (DP) mechanisms. Our approach incorporates DP-based noise injection methods, including Laplace and Gaussian distributions, into the data generation process. We then evaluate the utility of these DP-enhanced synthetic datasets by comparing the performance of ML models trained on them against models trained on the original data.
  arXiv  Detail & Related papers  (2024-12-30T01:10:10Z)
• Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data [51.41288763521186]
  Retrieval-augmented generation (RAG) enhances the outputs of language models by integrating relevant information retrieved from external knowledge sources. RAG systems may face severe privacy risks when retrieving private data. We propose using synthetic data as a privacy-preserving alternative for the retrieval data.
  arXiv  Detail & Related papers  (2024-06-20T22:53:09Z)
• Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision [76.4345564864002]
  Next-generation multiple input multiple output (MIMO) is expected to be intelligent and scalable. We propose the concept of the generative AI agent, which is capable of generating tailored and specialized contents. We present two compelling case studies that demonstrate the effectiveness of leveraging the generative AI agent for performance analysis.
  arXiv  Detail & Related papers  (2024-04-13T02:39:36Z)
• CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sources [5.898893619901382]
  We propose a framework for the collaborative and private generation of synthetic data from distributed data holders. We replace the trusted aggregator with secure multi-party computation protocols and output privacy via differential privacy (DP) We demonstrate the applicability and scalability of our approach for the state-of-the-art select-measure-generate algorithms MWEM+PGM and AIM.
  arXiv  Detail & Related papers  (2024-02-13T17:26:32Z)
• The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
  Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge. This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI) We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
  arXiv  Detail & Related papers  (2024-01-03T07:47:22Z)
• Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging [52.578054703818125]
  Artificial Intelligence (AI) models are vulnerable to information leakage of their training data, which can be highly sensitive. Differential Privacy (DP) aims to circumvent these susceptibilities by setting a quantifiable privacy budget. We show that using very large privacy budgets can render reconstruction attacks impossible, while drops in performance are negligible.
  arXiv  Detail & Related papers  (2023-12-05T12:21:30Z)
• Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective [28.968233485060654]
  We discuss the multifaceted challenges of privacy and copyright protection within the data lifecycle. We advocate for integrated approaches that combines technical innovation with ethical foresight. This work aims to catalyze a broader discussion and inspire concerted efforts towards data privacy and copyright integrity in Generative AI.
  arXiv  Detail & Related papers  (2023-11-30T05:03:08Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• Security and Privacy on Generative Data in AIGC: A Survey [17.456578314457612]
  We review the security and privacy on generative data in AIGC. We reveal the successful experiences of state-of-the-art countermeasures in terms of the foundational properties of privacy, controllability, authenticity, and compliance.
  arXiv  Detail & Related papers  (2023-09-18T02:35:24Z)
• Privacy-preserving Artificial Intelligence Techniques in Biomedicine [3.908261721108553]
  Training an AI model on sensitive data raises concerns about the privacy of individual participants. This paper provides a structured overview of advances in privacy-preserving AI techniques in biomedicine. It places the most important state-of-the-art approaches within a unified taxonomy and discusses their strengths, limitations, and open problems.
  arXiv  Detail & Related papers  (2020-07-22T18:35:55Z)

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.