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Register a new userEpione: Lightweight Contact Tracing with Strong Privacy
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Added by
Ni Trieu
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Contact tracing is an essential tool in containing infectious diseases such as COVID-19. Many countries and research groups have launched or announced mobile apps to facilitate contact tracing by recording contacts between users with some privacy considerations. Most of the focus has been on using random tokens, which are exchanged during encounters and stored locally on users phones. Prior systems allow users to search over released tokens in order to learn if they have recently been in the proximity of a user that has since been diagnosed with the disease. However, prior approaches do not provide end-to-end privacy in the collection and querying of tokens. In particular, these approaches are vulnerable to either linkage attacks by users using token metadata, linkage attacks by the server, or false reporting by users. In this work, we introduce Epione, a lightweight system for contact tracing with strong privacy protections. Epione alerts users directly if any of their contacts have been diagnosed with the disease, while protecting the privacy of users contacts from both central services and other users, and provides protection against false reporting. As a key building block, we present a new cryptographic tool for secure two-party private set intersection cardinality (PSI-CA), which allows two parties, each holding a set of items, to learn the intersection size of two private sets without revealing intersection items. We specifically tailor it to the case of large-scale contact tracing where clients have small input sets and the servers database of tokens is much larger.
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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.
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.
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.
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.
The global health threat from COVID-19 has been controlled in a number of instances by large-scale testing and contact tracing efforts. We created this document to suggest three functionalities on how we might best harness computing technologies to supporting the goals of public health organizations in minimizing morbidity and mortality associated with the spread of COVID-19, while protecting the civil liberties of individuals. In particular, this work advocates for a third-party free approach to assisted mobile contact tracing, because such an approach mitigates the security and privacy risks of requiring a trusted third party. We also explicitly consider the inferential risks involved in any contract tracing system, where any alert to a user could itself give rise to de-anonymizing information. More generally, we hope to participate in bringing together colleagues in industry, academia, and civil society to discuss and converge on ideas around a critical issue rising with attempts to mitigate the COVID-19 pandemic.
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