Prink: $k_s$-Anonymization for Streaming Data in Apache Flink

TL;DR

Prink is a practical, semantics-aware ks-anonymization method for streaming data in Apache Flink, enabling privacy-preserving data processing with minimal performance impact in real-world applications.

Contribution

It introduces the first semantics-aware ks-anonymization for non-numerical streaming data with native Flink integration, improving privacy, data utility, and ease of deployment.

Findings

Allows inclusion of non-numerical data in anonymization

Provides discrete data points instead of aggregates

Demonstrates acceptable performance overheads in realistic settings

Abstract

In this paper, we present Prink, a novel and practically applicable concept and fully implemented prototype for ks-anonymizing data streams in real-world application architectures. Building upon the pre-existing, yet rudimentary CASTLE scheme, Prink for the first time introduces semantics-aware ks-anonymization of non-numerical (such as categorical or hierarchically generalizable) streaming data in a information loss-optimized manner. In addition, it provides native integration into Apache Flink, one of the prevailing frameworks for enterprise-grade stream data processing in numerous application domains. Our contributions excel the previously established state of the art for the privacy guarantee-providing anonymization of streaming data in that they 1) allow to include non-numerical data in the anonymization process, 2) provide discrete datapoints instead of aggregates, thereby facilitating flexible data use, 3) are applicable in real-world system contexts with minimal integration efforts, and 4) are experimentally proven to raise acceptable performance overheads and information loss in realistic settings. With these characteristics, Prink provides an anonymization approach which is practically feasible for a broad variety of real-world, enterprise-grade stream processing applications and environments.