• Source code license: Apache 2.0

The full set of documents for DP3T is at https://github.com/DP-3T/documents. Please refer to the technical documents and whitepapers for the descriptions of the implementation.

At its core, DP3T describes a decentralized framework for proximity tracing where the smart phones store all observed contacts in a local database. During periodic intervals, the apps poll a database of infected SKt vectors and check if they have been exposed to any of the beacons sent by an infected app.

One of the core challenges in designing and implementing such a framework is the large amount of unknowns. This mock implementation of the framework helps us identify and measure some of these unknowns so that we can carry out better decisions and fine tune our technical design. This mock implementation is not meant to be a real implementation.

The mock implementation focuses on the app side. The backend is extremely simple and may consist of just a set of flat files of <SK, day> tuples. The app periodically polls and downloads lists of newly infected, checking locally if they have been exposed.

The cryptographic primitives are implemented in the class DP3T.KeyStore. The main methods are rotate_SK which rotates the private SK to the next day and rotate_ephIDs which creates the set of ephIDs for the new day. These methods are straight forward implementations of the description in the whitepaper.

The contact tracing and management is implemented in the class DP3T.ContactManager. Newly received beacons come in through receive_scans where they are added to local observations. Each epoch, these local observations are evaluated in process_epoch and contacts that have been around long enough (there was sufficient contact) are added to the daily observed contacts. The check_infected method takes an SK and date and checks all local contacts if one of the EphIDs of that SK were observed and then issues a warning.

You can run the DP3T reference implementation with your favorite python3 interpreter. The only additional package you need is Cryptodome. You can install it, e.g., through sudo apt install python3-pycryptodome or pip install pycryptodome.

The file example_run.py show cases the different aspects of proximity tracing. The three persons Alice, Bob, and Isidor interact in different settings and record the corresponding ephemeral IDs. At one point, Isidor is tested positive and agrees to submit his SK_t. Next, Alice and Bob test for this SK_t and Bob detects that he was at risk 1 day ago.

You can run the example with python3 example_run.py.