Examining Compliance with Personal Data Protection Regulations in Interorganizational Data Analysis

Szu-Chuang Li, Yi-Wen Chen and Yennun Huang
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Szu-Chuang Li: Department of Information and Communication, Tamkang University, New Taipei City 237, Taiwan
Yi-Wen Chen: Department of Information and Communication, Tamkang University, New Taipei City 237, Taiwan
Yennun Huang: CITI, Academia Sinica, Taipei 115, Taiwan

Sustainability, 2021, vol. 13, issue 20, 1-16

Abstract: The development of big data analysis technologies has changed how organizations work. Tech giants, such as Google and Facebook, are well positioned because they possess not only big data sets but also the in-house capability to analyze them. For small and medium-sized enterprises (SMEs), which have limited resources, capacity, and a relatively small collection of data, the ability to conduct data analysis collaboratively is key. Personal data protection regulations have become stricter due to incidents of private data being leaked, making it more difficult for SMEs to perform interorganizational data analysis. This problem can be resolved by anonymizing the data such that reidentifying an individual is no longer a concern or by deploying technical procedures that enable interorganizational data analysis without the exchange of actual data, such as data deidentification, data synthesis, and federated learning. Herein, we compared the technical options and their compliance with personal data protection regulations from several countries and regions. Using the EU’s GDPR (General Data Protection Regulation) as the main point of reference, technical studies, legislative studies, related regulations, and government-sponsored reports from various countries and regions were also reviewed. Alignment of the technical description with the government regulations and guidelines revealed that the solutions are compliant with the personal data protection regulations. Current regulations require “reasonable” privacy preservation efforts from data controllers; potential attackers are not assumed to be experts with knowledge of the target data set. This means that relevant requirements can be fulfilled without considerably sacrificing data utility. However, the potential existence of an extremely knowledgeable adversary when the stakes of data leakage are high still needs to be considered carefully.

Keywords: personal data protection; privacy; federated learning; data deidentification (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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