WeTrace: A privacy-preserving tracing approach

For the protection of people and society against harm and health threats — especially in case of the COVID-19 pandemic — a variety of different disciplines needs to be involved. The data collection of basic and health-related data of individuals in today's highly mobile society does help to plan, protect, and identify next steps health authorities and governments can, shall, or need to plan for or even implement. Thus, every individual, human, and inhabitant of the world is the key player — very different from many past crises'. And since all individuals are involved his/her (a) health and (b) privacy shall be considered in a very carefully crafted balance, not overruling one aspect with another one. Privacy remains key. The solution of the current pandemic's data collection can be based on a fully privacy-preserving application, which can be used by individuals on their mobile devices, such as smartphones, while maintaining at the same time their privacy. Additionally, respective data collected in such a fully distributed setting does help to confine the pandemic and can be achieved in a democratic and very open, but still and especially privacy-protecting manner. Therefore, the WeTrace approach and application designed utilizes the Bluetooth low energy (BLE) communication channel, many modern mobile devices offer, where public-key cryptography is being applied to allow for deciphering of messages for that destination it had been intended for. Since literally every other potential participant only listens to random data, even a brute force attack will not succeed. WeTrace and its Open Source implementation ensure that any receiver of a message knows that this is for him/her, without being able to identify the original sender.

[1]  Christof Röhrig,et al.  Advertising power consumption of bluetooth low energy systems , 2016, 2016 3rd International Symposium on Wireless Systems within the Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS).

[2]  Choong Seon Hong,et al.  When CVaR Meets With Bluetooth PAN: A Physical Distancing System for COVID-19 Proactive Safety , 2021, IEEE Sensors Journal.

[3]  Nigel P. Smart,et al.  Cryptography Made Simple , 2015, Information Security and Cryptography.

[4]  T. Meyyappan,et al.  Anonymization technique through record elimination to preserve privacy of published data , 2013, 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering.

[5]  Helge Janicke,et al.  A Survey of COVID-19 Contact Tracing Apps , 2020, IEEE Access.

[6]  Richard K. Lomotey,et al.  Energy Consumption Cost Analysis of Mobile Data Encryption and Decryption , 2016, 2016 IEEE International Conference on Mobile Services (MS).

[7]  J. Rocklöv,et al.  High population densities catalyze the spread of COVID-19 , 2020, Journal of travel medicine.

[8]  Georgios Kambourakis,et al.  Demystifying COVID-19 digital contact tracing: A survey on frameworks and mobile apps , 2020, Wireless Communications and Mobile Computing.

[9]  Tom Coughlin How Much Memory Do Cell Phones Need? , 2020, IEEE Consumer Electronics Magazine.

[10]  Mohammad Masoud,et al.  The power consumption cost of data encryption in smartphones , 2015, 2015 International Conference on Open Source Software Computing (OSSCOM).

[11]  A. Gassmann,et al.  WeTrace - A Privacy-preserving Mobile COVID-19 Tracing Approach and Application , 2020, ArXiv.

[12]  Gabriel Alves,et al.  Impact of capacity and discharging rate on battery life time: A stochastic model to support mobile device autonomy planning , 2017, Pervasive Mob. Comput..

[13]  Carmela Troncoso,et al.  Decentralized Privacy-Preserving Proximity Tracing , 2020, IEEE Data Eng. Bull..

[14]  Samee Ullah Khan,et al.  > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 , 2008 .