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The DtMin Protocol: Implementing data minimization Principles in Medical Information Sharing

ELECTRONICS 2025年第8期14卷 1501-1501页

作者： Park, Hyun-A Honam Univ Coll AI Convergence Dept Comp Engn 120Honamdae Gil Gwangju 62399 South Korea

This study proposes DtMin, a novel protocol for implementing data minimization principles in medical information sharing between healthcare providers (HCPs) and electronic health record providers (EHRPs). DtMin utilizes a multi-type encryption approach, combining attribute-based encryption (ABE) and hybrid encryption techniques. The protocol classifies patient data attributes into six categories based on sensitivity, consent status, and sharing requests. It then applies differential encryption methods to ensure only the intersection of patient-consented and EHRP-requested attributes is shared in decipherable form. DtMin's security is formally analyzed and proven under the ICR-DB and ICR-IS security games. Performance analysis demonstrates efficiency across various data volumes and patient numbers. This study explores the integration of DtMin with advanced cryptographic techniques such as lattice-based ABE and lightweight ABE variants, which can potentially enhance its performance and security in complex healthcare environments. Furthermore, it proposes strategies for integrating DtMin with existing healthcare information systems and adapting it to future big data environments processing over 100,000 records. These enhancements and integration strategies position DtMin as a scalable and practical solution for implementing data minimization in diverse healthcare settings, from small clinics to large-scale health information exchanges.

关键词： data minimization medical information sharing ABE multi-type attributes and encryption

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Privacy 3.0 := data minimization

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IT-INFORMATION TECHNOLOGY 2011年第1期53卷 34-40页

作者： Borcea-Pfitzmann, Katrin Pfitzmann, Andreas Berg, Manuela Tech Univ Dresden D-01062 Dresden Germany

Over the last two decades, privacy has been fading away. Some people have even stated: You have zero privacy - get over it! As privacy researchers, we are not willing to accept this statement. Therefore, we analyze the causes for this fading away of privacy, and develop a set of approaches to preserve or even regain privacy. We argue that Privacy 3.0 should be a combination of (1) data minimization, (2) User control of personal information disclosure, and (3) Contextual integrity. data minimization is one of the main motivations for the development of privacy-enhancing technologies, which aim to limit collection and processing of personal data by data controllers. User control of personal information disclosure supports users in deciding which personal information is released to whom and in which situation. Contextual integrity provides a new quality of privacy by making the original context in which particular personal data have been generated easily accessible to all entities that are aware of that particular personal data.

关键词： Privacy data minimization user control privacy-related context contextual integrity

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Embedding Personal data minimization Technologies in Organizations: Needs, Vision and Artifacts 14

Embedding Personal Data Minimization Technologies in Organiz...

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14th International Conference on Theory and Practice of Electronic Governance (ICEGOV)

作者： Bargh, Mortaza S. Meijer, Ronald van den Braak, Susan Latenko, Alexander Vink, Marco Choenni, Sunil Minist Justice & Secur Res & Documentat Ctr The Hague Netherlands Rotterdam Univ Applies Sci Res Ctr Creating 010 Rotterdam Netherlands

ISBN: (纸本)9781450390118

Often collected data sets contain more personal information than needed for a certain purpose. According to privacy laws and regulations the personal data in a data set should be minimized only to the data that are required and allowed for a chosen (legitimate) data usage. Statistical Disclosure Control (SDC) is one of the main data minimization technologies. Applying minimization technologies, particularly the SDC technology, into practice and embedding them within organizational settings can be a complex and demanding task for non-experts (i.e., those without prior experience and affinity with those technologies). In case of the SDC technology, for example, this challenge stems from its complexity, its context-dependency, its multi-disciplinarity, and its liability and accountability burdens. In this contribution we explain why personal data minimization is necessary, review the types of technologies for personal data minimization, present a framework we envision for embedding SDC technology in organizations, and mention the artifacts that, based on technology adoption theories, we have developed for embedding SDC technology within organizations. The envisioned framework comprises a structural model for deploying SDC technology and an iterative process for evolutionary organizational learning. At the end, we discuss the results obtained so far and mention some future research directions.

关键词： artifacts data minimization organization personal data protection statistical disclosure control technology adoption

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Towards a Formal Approach for data minimization in Programs (Short Paper) 16th

Towards a Formal Approach for Data Minimization in Programs ...

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26th European Symposium on Research in Computer Security (ESORICS) / 16th data Privacy Management International Workshop (DPM) / 5th International Workshop on Cryptocurrencies and Blockchain Technology (CBT)

作者： Lanzinger, Florian Weigl, Alexander Karlsruhe Inst Technol KIT Inst Informat Secur & Dependabil KASTEL Karlsruhe Germany

ISBN: (纸本)9783030939441;9783030939434

As more and more processes are digitized, the protection of personal data becomes increasingly important for individuals, agencies, companies, and society in general. One principle of data protection is data minimization, which limits the processing and storage of personal data to the minimum necessary for the defined purpose. To adhere to this principle, an analysis of what data are needed by a piece of software is required. In this paper, we present an idea for a program analysis which connects data minimization with secure information flow to assess which personal data are required by a program: A program is decomposed into two programs. The first projects the original input, keeping only the minimal amount of required data. The second computes the original output from the projected input. Thus, we achieve a program variant which is compliant with data minimization. We define the approach, show how it can be used for different scenarios, and give examples for how to compute such a decomposition.

关键词： Secure information flow data minimization GDPR

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Distributed data minimization for Decentralized Collaborative Filtering Systems 23

Distributed Data Minimization for Decentralized Collaborativ...

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24th International Conference on Distributed Computing and Networking (ICDCN)

作者： Eichinger, Tobias Kuepper, Axel Tech Univ Berlin Serv Ctr Networking Berlin Germany

ISBN: (纸本)9781450397964

data minimization is a legal principle that mandates the limitation of personal data to a necessary minimum in order to protect the privacy of individuals. In this light, we address ourselves to decentralized collaborative filtering systems in which individual users interact with each other instead of institutional recommendation providers to obtain recommendations. Decentralized collaborative filtering systems, in which data is distributed over all users, bear a privacy benefit over traditional centralized systems in which data is kept at a single authoritative node. We propose the first formal definition of data minimization for use in distributed systems in order to combine the privacy benefits of data minimization and data distribution. On the basis of this definition, we propose a distributed data minimization scheme based on Distributed Gradient Descent (DGD) that solves the data minimization problem for decentralized collaborative filtering systems. Here, data minimization represents a collection minimization problem in which users aim to collect necessary minimum amounts of rating data from other users in the system. We find by means of simulation that the overall amount of data collected by all users can be reduced significantly (> 14.6%) without jeopardizing recommendation performance, when users coordinate data collection. We thus prove that users collect unnecessarily much data, when they do not coordinate data collection.

关键词： data minimization recommender systems decentralized collaborative filtering distributed systems distributed gradient descent redundancy minimization

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Explainable Artificial Intelligence for Interpretable data minimization 23

Explainable Artificial Intelligence for Interpretable Data M...

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23rd IEEE International Conference on data Mining (IEEE ICDM)

作者： Becker, Maximilian Toprak, Emrah Beyerer, Juergen Karlsruhe Inst Technol Vis & Fus Lab Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany

ISBN: (纸本)9798350381641

Black box models such as deep neural networks are increasingly being deployed in high-stakes fields, including justice, health, and finance. Furthermore, they require a huge amount of data, and such data often contains personal information. However, the principle of data minimization in the European Union's General data Protection Regulation requires collecting only the data that is essential to fulfilling a particular purpose. Implementing data minimization for black box models can be difficult because it involves identifying the minimum set of variables that are relevant to the model's prediction, which may not be apparent without access to the model's inner workings. In addition, users are often reluctant to share all their personal information. We propose an interactive system to reduce the amount of personal data by determining the minimal set of features required for a correct prediction using explainable artificial intelligence techniques. Our proposed method can inform the user whether the provided variables contain enough information for the model to make accurate predictions or if additional variables are necessary. This humancentered approach can enable providers to minimize the amount of personal data collected for analysis and may increase the user's trust and acceptance of the system.

关键词： XAI data minimization Counterfactual Explanations

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Demo : data minimization and Informed Consent in Administrative Forms 23

Demo : Data Minimization and Informed Consent in Administrat...

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30th ACM SIGSAC Conference on Computer and Communications Security (ACM CCS)

作者： Anciaux, Nicolas Frittella, Sabine Joffroy, Baptiste Nguyen, Benjamin INRIA Saclay France Univ Paris Saclay Versailles Saclay France INSA Ctr Val Loire LIFO Bourges France Univ Orleans Orleans France

ISBN: (纸本)9798400700507

This article proposes a demonstration implementing the data minimization privacy principle, focusing on reducing data collected by government administrations through forms. data minimization is defined in many privacy regulations worldwide, but has not seen extensive real-world application. We propose a model based on logic and game theory and show that it is possible to create a practical and efficient solution for a real French welfare benefit case.

关键词： Privacy data minimization GDPR Informed consent

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Operationalizing the Legal Principle of data minimization for Personalization 20

Operationalizing the Legal Principle of Data Minimization fo...

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43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Biega, Asia J. Potash, Peter Daume, Hal, III Diaz, Fernando Finck, Michele Microsoft Res Montreal PQ Canada Univ Maryland Microsoft Res NYC College Pk MD 20742 USA Max Planck Inst Innovat & Competit Berlin Germany

ISBN: (纸本)9781450380164

Article 5(1)(c) of the European Union's General data Protection Regulation (GDPR) requires that "personal data shall be [...] adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed ('data minimisation')". To date, the legal and computational definitions of 'purpose limitation' and 'data minimization' remain largely unclear. In particular, the interpretation of these principles is an open issue for information access systems that optimize for user experience through personalization and do not strictly require personal data collection for the delivery of basic service. In this paper, we identify a lack of a homogeneous interpretation of the data minimization principle and explore two operational definitions applicable in the context of personalization. The focus of our empirical study in the domain of recommender systems is on providing foundational insights about the (i) feasibility of different data minimization definitions, (ii) robustness of different recommendation algorithms to minimization, and (iii) performance of different minimization strategies. We find that the performance decrease incurred by data minimization might not be substantial, but that it might disparately impact different users-a finding which has implications for the viability of different formal minimization definitions. Overall, our analysis uncovers the complexities of the data minimization problem in the context of personalization and maps the remaining computational and regulatory challenges.

关键词： GDPR data minimization Purpose Limitation Personalization

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From Principle to Practice: Vertical data minimization for Machine Learning 45

From Principle to Practice: Vertical Data Minimization for M...

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45th IEEE Symposium on Security and Privacy (SP)

作者： Staab, Robin Joyanovic, Nikola Balunovic, Mislay Vechev, Martin Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9798350331318;9798350331301

Aiming to train and deploy predictive models, organizations collect large amounts of detailed client data, risking the exposure of private information in the event of a breach. To mitigate this, policymakers increasingly demand compliance with the data minimization (DM) principle, restricting data collection to only that data which is relevant and necessary for the task. Despite regulatory pressure, the problem of deploying machine learning models that obey DM has so far received little attention. In this work, we address this challenge in a comprehensive manner. We propose a novel vertical DM (vDM) workflow based on data generalization, which by design ensures that no full-resolution client data is collected during training and deployment of models, benefiting client privacy by reducing the attack surface in case of a breach. We formalize and study the corresponding problem of finding generalizations that both maximize data utility and minimize empirical privacy risk, which we quantify by introducing a diverse set of policy-aligned adversarial scenarios. Finally, we propose a range of baseline vDM algorithms, as well as Privacy-aware Tree (PAT), an especially effective vDM algorithm that outperforms all baselines across several settings. We plan to release our code as a publicly available library, helping advance the standardization of DM for machine learning. Overall, we believe our work can help lay the foundation for further exploration and adoption of DM principles in real-world applications.

关键词： data minimization machine learning privacy privacy-preserving technologies

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Configurable Per-Query data minimization for Privacy-Compliant Web APIs 22nd

Configurable Per-Query Data Minimization for Privacy-Complia...

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22nd International Conference on Web Engineering (ICWE)

作者： Pallas, Frank Hartmann, David Heinrich, Paul Kipke, Josefine Gruenewald, Elias TU Berlin Informat Syst Engn Berlin Germany

ISBN: (纸本)9783031099175;9783031099168

The purpose of regulatory data minimization obligations is to limit personal data to the absolute minimum necessary for a given context. Beyond the initial data collection, storage, and processing, data minimization is also required for subsequent data releases, as it is the case when data are provided using query-capable Web APIs. data-providing Web APIs, however, typically lack sophisticated data minimization features, leaving the task open to manual and all too often missing implementations. In this paper, we address the problem of data minimization for data-providing, query-capable Web APIs. Based on a careful analysis of functional and non-functional requirements, we introduce Janus, an easy-to-use, highly configurable solution for implementing legally compliant data minimization in GraphQL Web APIs. Janus provides a rich set of information reduction functionalities that can be configured for different client roles accessing the API. We present a technical proof-of-concept along with experimental measurements that indicate reasonable overheads. Janus is thus a practical solution for implementing GraphQL APIs in line with the regulatory principle of data minimization.

关键词： Privacy data protection data minimization Anonymization Web APIs GraphQL Privacy Engineering

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