Provable Privacy Guarantee for Individual Identities and Locations in Large-Scale Contact Tracing

• URL: http://arxiv.org/abs/2409.12341v1
• Date: Wed, 18 Sep 2024 22:19:48 GMT
• Title: Provable Privacy Guarantee for Individual Identities and Locations in Large-Scale Contact Tracing
• Authors: Tyler Nicewarner, Wei Jiang, Aniruddha Gokhale, Dan Lin,
• Abstract summary: Our paper proposes a highly scalable, practical contact tracing system called PREVENT. It can work with a variety of location collection methods to gain a comprehensive overview of a person's trajectory. Our system is very efficient and can provide real-time query services for large-scale datasets with millions of locations.
• Score: 4.436902019991021
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The task of infectious disease contact tracing is crucial yet challenging, especially when meeting strict privacy requirements. Previous attempts in this area have had limitations in terms of applicable scenarios and efficiency. Our paper proposes a highly scalable, practical contact tracing system called PREVENT that can work with a variety of location collection methods to gain a comprehensive overview of a person's trajectory while ensuring the privacy of individuals being tracked, without revealing their plain text locations to any party, including servers. Our system is very efficient and can provide real-time query services for large-scale datasets with millions of locations. This is made possible by a newly designed secret-sharing based architecture that is tightly integrated into unique private space partitioning trees. Notably, our experimental results on both real and synthetic datasets demonstrate that our system introduces negligible performance overhead compared to traditional contact tracing methods. PREVENT could be a game-changer in the fight against infectious diseases and set a new standard for privacy-preserving location tracking.

Related papers

• 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)
• Masked Differential Privacy [64.32494202656801]
  We propose an effective approach called masked differential privacy (DP), which allows for controlling sensitive regions where differential privacy is applied. Our method operates selectively on data and allows for defining non-sensitive-temporal regions without DP application or combining differential privacy with other privacy techniques within data samples.
  arXiv  Detail & Related papers  (2024-10-22T15:22:53Z)
• Measuring Privacy Loss in Distributed Spatio-Temporal Data [26.891854386652266]
  We propose an alternative privacy loss against location reconstruction attacks by an informed adversary. Our experiments on real and synthetic data demonstrate that our privacy loss better reflects our intuitions on individual privacy violation in the distributed setting.
  arXiv  Detail & Related papers  (2024-02-18T09:53:14Z)
• 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)
• Accurate and Efficient Trajectory-based Contact Tracing with Secure Computation and Geo-Indistinguishability [15.12803268418723]
  Trajectory-based contact tracing allows the tracing of both direct contacts and indirect contacts. Traditional Secure Multiparty Computation (MPC) techniques suffer from prohibitive running time. We propose a technical framework called ContactGuard to achieve accurate, efficient, and privacy-preserving trajectory-based contact tracing.
  arXiv  Detail & Related papers  (2023-03-06T02:17:38Z)
• Private Set Generation with Discriminative Information [63.851085173614]
  Differentially private data generation is a promising solution to the data privacy challenge. Existing private generative models are struggling with the utility of synthetic samples. We introduce a simple yet effective method that greatly improves the sample utility of state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-11-07T10:02:55Z)
• PCT-TEE: Trajectory-based Private Contact Tracing System with Trusted Execution Environment [20.089914572456546]
  Existing Bluetooth-based Private Contact Tracing (PCT) systems can detect whether people have come into direct contact with COVID-19 patients. We propose an efficient and secure contact tracing system that enables both direct contact and indirect contact.
  arXiv  Detail & Related papers  (2020-12-07T15:22:19Z)
• Another Look at Privacy-Preserving Automated Contact Tracing [3.0718680861621404]
  A number of automated contact tracing solutions have been proposed and some have been deployed. Security and privacy issues of these solutions are still open and under intensive debate. We propose a venue-based ACT concept, which only monitors users' contacting history in virus-spreading-prone venues.
  arXiv  Detail & Related papers  (2020-10-26T09:59:15Z)
• PGLP: Customizable and Rigorous Location Privacy through Policy Graph [68.3736286350014]
  We propose a new location privacy notion called PGLP, which provides a rich interface to release private locations with customizable and rigorous privacy guarantee. Specifically, we formalize a user's location privacy requirements using a textitlocation policy graph, which is expressive and customizable. Third, we design a private location trace release framework that pipelines the detection of location exposure, policy graph repair, and private trajectory release with customizable and rigorous location privacy.
  arXiv  Detail & Related papers  (2020-05-04T04:25:59Z)
• Give more data, awareness and control to individual citizens, and they will help COVID-19 containment [74.10257867142049]
  Contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach raises concerns about citizens' privacy and needlessly strong digital surveillance. We advocate a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores"
  arXiv  Detail & Related papers  (2020-04-10T20:30:37Z)

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.