A Seed for Privacy-semi-automatic privacy-revealing data detection in databases and data streams

Jul 16, 2026

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Sharing databases and data streams creates privacy risks from complex events that expose both individual data elements and their combinations. Accurately identifying these privacy-revealing complex events is essential for preserving privacy without destroying data utility, yet data producers often cannot label such events comprehensively. pArborist semiautomatically generates seed-guided query sets and refines them by eliminating non-correlated or conditionally independent queries, yielding strong recall and precision and GDPR-aligned detection.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

Introduction

Privacy risk in data sharing

Limits of anonymization

Need for privacy-revealing event detection

Overview of pArborist

A Seed for Privacy-semi-automatic privacy-revealing data detection in databases and data streams He Gu Thomas Plagemann Vera Goebel heg,plageman, [goebel@ifi.uio.no](goebel@ifi.uio.no) University of Olso Oslo, Norway arXiv :2607 .0880 1v 1 [ cs .CR] 9 Jul 2026 ABSTRACT Sharing databases and data streams imposes the danger of revealing private information in the form of complex events which can comprise individual data elements and their combinations. Identifying these privacy-revealing complex events is crucial for preserving privacy while maintaining data utility. However, data producers often lack the expertise to comprehensively identify these events, which undermines many state-of-the-art privacy-preserving mechanisms that rely on accurate event labeling. To address this challenge, we developed pArborist-a tool that can semi-automatically create a set of queries to identify and label privacy-revealing complex events in both static datasets and dynamic data streams, guided by the privacy requirements of the data producer. pArborist uses the schema of the database or data stream combined with initial input from the data producer, i.e., seed queries. From each seed query, pArborist grows a tree containing all possible syntactically correct queries, constrained by an upper limit on computational resources. Following this growing phase, the tree is refined by eliminating queries that lack correlation to the seed or are conditionally independent of the seed. Our evaluation indicates that pArborist achieves overall recall of 90% and precision of 93% in finding privacy-revealing queries, and this significantly surpasses the state-of-the-art approach FQID. In data stream processing experiments, pArborist introduces a delay of approximately 1.3 ms following an average warm-up period of 920 ms. The experiments also show that pArborist can automatically detect privacy-revealing complex events according to GDPR. CCS CONCEPTS • Security and privacy → Formal methods and theory of security; Data anonymization and sanitization; • Information systems → Data management systems. KEYWORDS privacy, GDPR, security, database, data stream ACM Reference Format: He Gu, Thomas Plagemann, and Vera Goebel. 2026. A Seed for Privacy-semi-automatic privacy-revealing data detection in databases and data Please use nonacm option or ACM Engage class to enable CC licenses This work is licensed under a Creative Commons Attribution-NonCommercialNoDerivatives 4 .0 International License. Conference’17, July 2017, Washington, DC, USA © 2026 Copyright held by the owner/author(s) . ACM ISBN 978-x-xxxx-xxxx-x/YY/MM [https://doi.org/10.1145/nnnnnnn.nnnnnnn](https://doi.org/10.1145/nnnnnnn.nnnnnnn) streams. In . ACM, New York, NY, USA, 12 pages. [https://doi.org/10.1145/](https://doi.org/10.1145/)[ ](https://doi.org/10.1145/)nnnnnnn.nnnnnnn 1 INTRODUCTION Data sharing is an essential procedure for most data-driven applications and research, which raises great concerns about potential privacy disclosures. A typical approach to protecting private information is anonymization. In practice, anonymization is achieved by protecting identifiers and quasi-identifier. Identifiers denote the attributes that reveal the identity of an individual, while quasiidentifiers are the combinations of certain attributes that jointly reveal the identity of an individual. Detecting identifiers and quasiidentifiers during anonymization is a nontrivial research problem and is still a subject to research [2, 7, 18] . However, anonymization is not always suitable for privacy protections. Consider the case of elderly people living alone. Health monitoring devices continuously send information about the daily activities of the elderly to health personnel to determine whether they need assistance. In such cases, the devices and their users have a strong incentive to share the data without concealing the identity of users. However, there may be some part

FAQs

What privacy problem does pArborist target in shared databases and data streams?

It targets privacy disclosure caused by privacy-revealing complex events, which can leak information through specific data elements and their combinations.

How does pArborist generate and refine candidate queries?

It starts from seed queries provided by the data producer, grows a tree of syntactically correct queries within resource limits, then refines by removing queries that lack correlation to the seed or are conditionally independent.

What performance and compliance results are reported for pArborist?

The evaluation reports about 90% overall recall and 93% precision for finding privacy-revealing queries, and data stream experiments show GDPR-consistent detection with an added processing delay after warm-up.

Jul 16, 2026

Upload by Levi