Cardinality Counting in "Alcatraz": A Privacy-aware Federated Learning Approach

Nan Wu, Xin Yuan, Shuo Wang, Hongsheng Hu, Jason Xue

Keywords: Differential privacy, federated learning, data privacy

TL;DR: This paper proposes a privacy-preserving federated clustering approach for cardinality counting.

Abstract: The task of cardinality counting, pivotal for data analysis, endeavors to quantify unique elements within datasets and has significant applications across various sectors like healthcare, marketing, cybersecurity, and web analytics. Current methods, categorized into deterministic and probabilistic, often fail to prioritize data privacy. Given the fragmentation of datasets across various organizations, there is an elevated risk of inadvertently disclosing sensitive information during collaborative data studies using state-of-the-art cardinality counting techniques. This study introduces an innovative privacy-centric solution for the cardinality counting dilemma, leveraging a federated learning framework. Our approach involves employing a locally differentially private data encoding for initial processing, followed by a privacy-aware federated $K$-means clustering strategy, ensuring that cardinality counting occurs across distinct datasets without necessitating data amalgamation. The efficacy of our methodology is underscored by promising results from tests on both real-world and simulated datasets, pointing towards a transformative approach to privacy-sensitive cardinality counting in contemporary data science.

Track: Systems and Infrastructure for Web, Mobile, and WoT

Submission Guidelines Scope: Yes

Submission Guidelines Blind: Yes

Submission Guidelines Format: Yes

Submission Guidelines Limit: Yes

Submission Guidelines Authorship: Yes

Student Author: No

Submission Number: 2039