Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userDecentralized Privacy-Preserving Proximity Tracing
81
0
0.0
(
0
)
Added by
Michael Veale
Publication date
2020
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
This document describes and analyzes a system for secure and privacy-preserving proximity tracing at large scale. This system, referred to as DP3T, provides a technological foundation to help slow the spread of SARS-CoV-2 by simplifying and accelerating the process of notifying people who might have been exposed to the virus so that they can take appropriate measures to break its transmission chain. The system aims to minimise privacy and security risks for individuals and communities and guarantee the highest level of data protection. The goal of our proximity tracing system is to determine who has been in close physical proximity to a COVID-19 positive person and thus exposed to the virus, without revealing the contacts identity or where the contact occurred. To achieve this goal, users run a smartphone app that continually broadcasts an ephemeral, pseudo-random ID representing the users phone and also records the pseudo-random IDs observed from smartphones in close proximity. When a patient is diagnosed with COVID-19, she can upload pseudo-random IDs previously broadcast from her phone to a central server. Prior to the upload, all data remains exclusively on the users phone. Other users apps can use data from the server to locally estimate whether the devices owner was exposed to the virus through close-range physical proximity to a COVID-19 positive person who has uploaded their data. In case the app detects a high risk, it will inform the user.
rate research
Read More
During a pandemic, contact tracing is an essential tool to drive down the infection rate within a population. To accelerate the laborious manual contact tracing process, digital contact tracing (DCT) tools can track contact events transparently and privately by using the sensing and signaling capabilities of the ubiquitous cell phone. However, an effective DCT must not only preserve user privacy but also augment the existing manual contact tracing process. Indeed, not every member of a population may own a cell phone or have a DCT app installed and enabled. We present KHOVID to fulfill the combined goal of manual contact-tracing interoperability and DCT user privacy. At KHOVIDs core is a privacy-friendly mechanism to encode user trajectories using geolocation data. Manual contact tracing data can be integrated through the same geolocation format. The accuracy of the geolocation data from DCT is improved using Bluetooth proximity detection, and we propose a novel method to encode Bluetooth ephemeral IDs. This contribution describes the detailed design of KHOVID; presents a prototype implementation including an app and server software; and presents a validation based on simulation and field experiments. We also compare the strengths of KHOVID with other, earlier proposals of DCT.
Activity-tracking applications and location-based services using short-range communication (SRC) techniques have been abruptly demanded in the COVID-19 pandemic, especially for automated contact tracing. The attention from both public and policy keeps raising on related practical problems, including textit{1) how to protect data security and location privacy? 2) how to efficiently and dynamically deploy SRC Internet of Thing (IoT) witnesses to monitor large areas?} To answer these questions, in this paper, we propose a decentralized and permissionless blockchain protocol, named textit{Bychain}. Specifically, 1) a privacy-preserving SRC protocol for activity-tracking and corresponding generalized block structure is developed, by connecting an interactive zero-knowledge proof protocol and the key escrow mechanism. As a result, connections between personal identity and the ownership of on-chain location information are decoupled. Meanwhile, the owner of the on-chain location data can still claim its ownership without revealing the private key to anyone else. 2) An artificial potential field-based incentive allocation mechanism is proposed to incentivize IoT witnesses to pursue the maximum monitoring coverage deployment. We implemented and evaluated the proposed blockchain protocol in the real-world using the Bluetooth 5.0. The storage, CPU utilization, power consumption, time delay, and security of each procedure and performance of activities are analyzed. The experiment and security analysis is shown to provide a real-world performance evaluation.
In the current COVID-19 pandemic, manual contact tracing has been proven very helpful to reach close contacts of infected users and slow down virus spreading. To improve its scalability, a number of automated contact tracing (ACT) solutions have proposed and some of them have been deployed. Despite the dedicated efforts, security and privacy issues of these solutions are still open and under intensive debate. In this paper, we examine the ACT concept from a broader perspective, by focusing on not only security and privacy issues but also functional issues such as interface, usability and coverage. We first elaborate on these issues and particularly point out the inevitable privacy leakages in existing BLE-based ACT solutions. Then, we propose a venue-based ACT concept, which only monitors users contacting history in virus-spreading-prone venues and is able to incorporate different location tracking technologies such as BLE and WIFI. Finally, we instantiate the venue-based ACT concept and show that our instantiation can mitigate most of the issues we have identified in our analysis.
The infection rate of COVID-19 and lack of an approved vaccine has forced governments and health authorities to adopt lockdowns, increased testing, and contact tracing to reduce the spread of the virus. Digital contact tracing has become a supplement to the traditional manual contact tracing process. However, although there have been a number of digital contact tracing apps proposed and deployed, these have not been widely adopted owing to apprehensions surrounding privacy and security. In this paper, we propose a blockchain-based privacy-preserving contact tracing protocol, Did I Meet You (DIMY), that provides full-lifecycle data privacy protection on the devices themselves as well as on the back-end servers, to address most of the privacy concerns associated with existing protocols. We have employed Bloom filters to provide efficient privacy-preserving storage, and have used the Diffie-Hellman key exchange for secret sharing among the participants. We show that DIMY provides resilience against many well known attacks while introducing negligible overheads. DIMYs footprint on the storage space of clients devices and back-end servers is also significantly lower than other similar state of the art apps.
Online advertising fuels the (seemingly) free internet. However, although users can access most of the web services free of charge, they pay a heavy coston their privacy. They are forced to trust third parties and intermediaries, who not only collect behavioral data but also absorb great amounts of ad revenues. Consequently, more and more users opt out from advertising by resorting to ad blockers, thus costing publishers millions of dollars in lost ad revenues. Albeit there are various privacy-preserving advertising proposals (e.g.,Adnostic, Privad, Brave Ads) from both academia and industry, they all rely on centralized management that users have to blindly trust without being able to audit, while they also fail to guarantee the integrity of the per-formance analytics they provide to advertisers. In this paper, we design and deploy THEMIS, a novel, decentralized and privacy-by-design ad platform that requires zero trust by users. THEMIS (i) provides auditability to its participants, (ii) rewards users for viewing ads, and (iii) allows advertisers to verify the performance and billing reports of their ad campaigns. By leveraging smart contracts and zero-knowledge schemes, we implement a prototype of THEMIS and early performance evaluation results show that it can scale linearly on a multi sidechain setup while it supports more than 51M users on a single-sidechain.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
