arXiv subjects

Youri Kaminsky

Publications and source records attributed to Youri Kaminsky.

3 recordsLinked to original sources

arXiv2026

Inference-Aware & Privacy-Preserving Deletion in Databases

Deletion is a fundamental database operation, yet modern systems often fail to provide the privacy guarantee that users expect from it. A deleted value may disappear from query results and even from physical storage, yet remain inferable from dependencies, derived data, or traces exposed by the deletion event itself. Meaningful deletion, therefore, requires more than logical removal or physical erasure; it requires a privacy guarantee that limits what remains inferable after deletion. In this paper, we take an inference-centric view of deletion, focusing on two leakage channels: leakage from the post-deletion state and leakage from the deletion pattern itself. We use this lens to distinguish logical, physical, and semantic deletion, organize the design space of deletion operations, and highlight open research challenges for building deletion mechanisms with meaningful privacy guarantees in database systems.

cs.DB↗

arXiv2025

Meaningful Data Erasure in the Presence of Dependencies

Data regulations like GDPR require systems to support data erasure but leave the definition of "erasure" open to interpretation. This ambiguity makes compliance challenging, especially in databases where data dependencies can lead to erased data being inferred from remaining data. We formally define a precise notion of data erasure that ensures any inference about deleted data, through dependencies, remains bounded to what could have been inferred before its insertion. We design erasure mechanisms that enforce this guarantee at minimal cost. Additionally, we explore strategies to balance cost and throughput, batch multiple erasures, and proactively compute data retention times when possible. We demonstrate the practicality and scalability of our algorithms using both real and synthetic datasets.

cs.DB↗

arXiv2024

Repairing Databases over Metric Spaces with Coincidence Constraints

Datasets often contain values that naturally reside in a metric space: numbers, strings, geographical locations, machine-learned embeddings in a Euclidean space, and so on. We study the computational complexity of repairing inconsistent databases that violate integrity constraints, where the database values belong to an underlying metric space. The goal is to update the database values to retain consistency while minimizing the total distance between the original values and the repaired ones. We consider what we refer to as \emph{coincidence constraints}, which include key constraints, inclusion, foreign keys, and generally any restriction on the relationship between the numbers of cells of different labels (attributes) coinciding in a single value, for a fixed attribute set. We begin by showing that the problem is APX-hard for general metric spaces. We then present an algorithm solving the problem optimally for tree metrics, which generalize both the line metric (i.e., where repaired values are numbers) and the discrete metric (i.e., where we simply count the number of changed values). Combining our algorithm for tree metrics and a classic result on probabilistic tree embeddings, we design a (high probability) logarithmic-ratio approximation for general metrics. We also study the variant of the problem where each individual value's allowed change is limited. In this variant, it is already NP-complete to decide the existence of any legal repair for a general metric, and we present a polynomial-time repairing algorithm for the case of a line metric.

cs.DB↗