Contribution to Data Minimization for Personal Data and Trade Secrets
To respond to tensions between personal data protection and trade secrets, ESR 1 Qifan Yang and ESR 4 Cristian Lepore participated in the WOPA 4 “Personal Data vs. Trade Secret.” Qifan’s research explores legal and economic solutions to balance personal data protection and market competition. Cristian’s research analyses technologies for data minimization. The link between the two is personal data. In the collaborative work, we investigate a new legal and technical tool to sketch boundaries between personal data vs. trade secrets and propose a GDPR-compliant model to protect personal data and trade secrets with legal and technical data minimization considerations. The title of the work is “Personal Data vs. Trade Secret.” The aim is to enhance privacy protection and effective competition in the marketplace, thus contributing to privacy protection and effective competition. To achieve the objective, ESR1 analyses legal tools to draw clearer boundaries between personal data and trade secrets while promoting data sharing to unleash competitive dynamics. On the other hand, ESR4 explores frameworks to achieve data minimization by default considering technical and legal aspects.
The work “Contribution to data minimization for personal data and trade secret” is closely related to Crossroad 1, “Privacy vs. Intellectual Property”, and Crossroad 3, “Data Ownership.” The former is an important principle to be observed by personal data controllers and processors in data processing. It plays a pivotal role in determining the scale of data collection, processing, storage, and availability. This principle prompts the trade secrets directive to be applied more rationally while respecting the data subject’s rights, so personal data can flow more freely with the individual’s intention, facilitating the improvements of products or services and promoting market competition. With the idea to strengthen the European digital single market, the EU Commission designed a cross-interoperability framework with privacy objectives in mind — here, data minimization plays a crucial role. The latter studies the work of the European Commission related to cross-border interoperability through coming standards from the academia and industry (e.g., the W3C VC model). Compared to other platforms, the data exchange format designed by the W3C with verifiable credentials is a significant advantage, opening up a better data minimization implementation. While the specific term “IP” is not explicitly mentioned in the provided text, the researchers’ work on data minimization and its impact on the classification of personal data as a trade secret aligns with the broader topic of “personal data vs IP.” They seek to address one of the important tensions between personal data protection and market competition, which are critical considering a data-driven society’s legal and ethical implications.
Abstract of the Working Paper:
Personal data is a resource with significant potential economic value and has a natural and intrinsic “bloodline” tied to personal privacy. The control over personal data, at the intersection of personal privacy and commercial assets, shapes market competition to slight advantages for market dominants. The data minimization from the General Data Protection Regulation as a general benchmark builds bridges between personal data and trade secrets and imprisons excessive breaches against personal data by trade secrets. Still, the legal and technical framework for achieving this target is left open. This work contribution is multifold. (1) It explores the intersection and relationship between personal data and trade secrets, specifically whether personal data can be considered a trade secret beyond personal data protection and minimization. (2) A high-quality GDPR-compliant teaching model is proposed to protect personal data and trade secrets with legal and technical data minimization considerations. (3) It presents the parallelisms between the European framework for electronic transactions and the introduced model. In the long run, we aim to provide citizens with tools to understand and mitigate privacy issues.