Balancing Privacy: Leveraging Privacy Budgets for Privacy-Enhancing Technologies

PETs ensure input privacy, enabling sharing of and computation on sensitive input data without revealing its contents or allowing deduction. However, PETs do not necessarily guarantee output privacy. Revealing the final result of a computation (the output) often allows some conclusions about the input data, which might compromise sensitive data. A critical (and sometimes overlooked) aspect in ensuring robust privacy preservation is to account for the privacy budget allocated in any given PETs project. The privacy budget refers to the finite amount of privacy protection that can be allocated when performing various computations or data analyses using PETs, in order to safeguard output privacy. In the talk we will start with some examples of how PETs don't protect output privacy and we will give a comprehensive overview of privacy budget allocation for PETs by reviewing different privacy metrics and a number of relevant policy and governance guidelines. We will discuss how the concept of a privacy budget can help to demonstrate compliance with the requirements of privacy by design and we will show how organizations can strike a balance between preserving the privacy of sensitive input data and deriving valuable insights from data analysis.