Formalizing Data Deletion in the Context of the Right to Be Forgotten

By Sanjam Garg, Shafi Goldwasser, Prashant Nalini Vasudevan. 2020

98 citations recorded by the source corpus.

The right of an individual to request the deletion of their personal data by an entity that might be storing it – referred to as the right to be forgotten – has been explicitly recognized, legislated, and exercised in several jurisdictions across the world, including the European Union, Argentina, and California. However, much of the discussion surrounding this right offers only an intuitive notion of what it means for it to be fulfilled – of what it means for such personal data to be deleted. In this work, we provide a formal definitional framework for the right to be forgotten using tools and paradigms from cryptography. In particular, we provide a precise definition of what could be (or should be) expected from an entity that collects individuals’ data when a request is made of it to delete some of this data. Our framework captures most, though not all, relevant aspects of typical systems involved in data processing. While it cannot be viewed as expressing the statements of current laws (especially since these are rather vague in this respect), our work offers technically precise definitions that represent possibilities for what the law could reasonably expect, and alternatives for what future versions of the law could explicitly require. Finally, with the goal of demonstrating the applicability of our framework and definitions, we consider various natural and simple scenarios where the right to be forgotten comes up. For each of these scenarios, we highlight the pitfalls that arise even in genuine attempts at implementing systems offering deletion guarantees, and also describe technological solutions that provably satisfy our definitions. These solutions bring together techniques built by various communities.

Subfields: Artificial Intelligence, Sociology and Political Science

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Citations in the corpus

Mixed-Privacy Forgetting in Deep Networks (2020, background): “Can we forget the cohort of interest without interfering with information about the other data and preserving, to the extent possible, the accuracy of the trained model? Recently, the problem of forgetting has received considerable attention [15, 16, 13, 19, 5, 24, 35, 41, 6, 39, 12, 7, 36], but sol”
Machine Unlearning for Random Forests (2020, background): “…companies to remove private user data upon request has prompted new discussions on data privacy and ownership (Shintre et al., 2019), and fulﬁlling this “right to be forgotten” (Kwak et al., 2017; Garg et al., 2020) may require updating any models trained on this data (Villaronga et al., 2018).”
Fast Yet Effective Machine Unlearning (2021, background): “[15] Sanjam Garg, Shafi Goldwasser, and Prashant Nalini Vasudevan.”
A Survey of Machine Unlearning (2022, unknown): “(Bounded/Certified Unlearning) Definition 1 ( 𝜖 -Approximate Unlearning). algorithm 𝐴 if ∀T ⊆ H ,𝐷 ∈ 𝑍 ∗ ,𝑧 ∈ 𝐷 :”
Formalizing Human Ingenuity: A Quantitative Framework for Copyright Law's Substantial Similarity (2022, background): “This works joins recent scholarship on using concepts and modeling principles from computer science to reason about law and policy questions in a new light [5, 16, 17, 33, 43, 68, 69, 81].”
DART: Data Addition and Removal Trees (2020, unknown): “Fulﬁlling this “right to be forgotten” (Garg, Goldwasser, and Vasudevan 2020; Kwak, Lee et al. 2017) may require updating any models trained on this data (Villaronga, Kieseberg, and Li 2018).”
Remember What You Want to Forget: Algorithms for Machine Unlearning (2021, background): “Garg et al. (2020) give an alternative perspective on machine unlearning, grounded in cryptography.”