Benny Pinkas (Bar-Ilan University); Eyal Ronen (Tel Aviv University)

In recent months multiple proposals for contact tracing schemes for combating the spread of COVID-19 have been published. Many of those proposals try to implement this functionality in a decentralized and privacy-preserving manner using Bluetooth Low Energy (BLE).

In this paper, we present “Hashomer”, our proposal for a contact tracing scheme tailored for the Israeli Ministry of Health’s (MoH) “Hamagen” application. The design is fully decentralized, and has the following properties:

- Message Unlinkability — Different BLE messages sent by the same user cannot be linked to each other (except for messages sent by COVID-19 positive users who give consent to tracing their contacts, and only for messages sent within a short time period).

- Explainability — To convince users that they were exposed to a COVID-19 positive person, we let them learn the approximate time of contact. This also implies that users can potentially learn, using the phone’s GPS information, the location of the exposure.

- Partial Disclosure and Coercion Prevention — Users and the MoH are able to redact tracing information and exposure notifications for specific time intervals.

- Prevention of Relay Attacks – The design prevents attacks where a malicious receiver relays BLE transmissions from one location to other locations.

- Proof of exposure to a COVID-19 positive person — To prevent false reports about exposure, we allow users who are notified by the application about an exposure to a COVID-19 positive person, to prove this fact to the server.

- Identity Commitment — To prevent malicious changing or replacing keys, we bind the BLE messages to a unique ID in a privacy-preserving way.

- Performance — BLE payload size is limited to 16 bytes. The application uses only symmetric key cryptography (AES and HMAC). To reduce bandwidth, contact updates from the MoH are of limited size. Moreover, the local search for exposure is linear in the number of messages and number of COVID-19 positive persons

View More Papers

Short Paper: Declarative Demand-Driven Reverse Engineering

Yihao Sun, Jeffrey Ching, Kristopher Micinski (Department of Electical Engineering and Computer Science, Syracuse University)

Read More

Taking a Closer Look at the Alexa Skill Ecosystem

Christopher Lentzsch (Ruhr-Universität Bochum), Anupam Das (North Carolina State University)

Read More

Differentially Private Health Tokens for Estimating COVID-19 Risk

David Butler, Chris Hicks, James Bell, Carsten Maple, and Jon Crowcroft (The Alan Turing Institute)

Read More

Demo #10: Security of Deep Learning based Automated Lane...

Takami Sato, Junjie Shen, Ningfei Wang (UC Irvine), Yunhan Jia (ByteDance), Xue Lin (Northeastern University), and Qi Alfred Chen (UC Irvine)

Read More