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Revolutionizing Medical Data Sharing Using Advanced Privacy-Enhancing Technologies: Technical, Legal, and Ethical Synthesis
Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data utility. The shortcomings of traditional privacy-enhancing technologies mean that institutions rely upon bespoke data sharing contracts. The lengthy process and administration induced by these contracts increases the inefficiency of data sharing and may disincentivize important clinical treatment and medical research. This paper provides a synthesis between 2 novel advanced privacy-enhancing technologies—homomorphic encryption and secure multiparty computation (defined together as multiparty homomorphic encryption). These privacy-enhancing technologies provide a mathematical guarantee of privacy, with multiparty homomorphic encryption providing a performance advantage over separately using homomorphic encryption or secure multiparty computation. We argue multiparty homomorphic encryption fulfills legal requirements for medical data sharing under the European Union’s General Data Protection Regulation which has set a global benchmark for data protection. Specifically, the data processed and shared using multiparty homomorphic encryption can be considered anonymized data. We explain how multiparty homomorphic encryption can reduce the reliance upon customized contractual measures between institutions. The proposed approach can accelerate the pace of medical research while offering additional incentives for health care and research institutes to employ common data interoperability standards.
Journal Article
Of privacy and power : the transatlantic struggle over freedom and security
We live in an interconnected world, where security problems like terrorism are spilling across borders, and globalized data networks and e-commerce platforms are reshaping the world economy. This means that states' jurisdictions and rule systems clash. How have they negotiated their differences over freedom and security? Of Privacy and Power investigates how the European Union and United States, the two major regulatory systems in world politics, have regulated privacy and security, and how their agreements and disputes have reshaped the transatlantic relationship. The transatlantic struggle over freedom and security has usually been depicted as a clash between a peace-loving European Union and a belligerent United States. Henry Farrell and Abraham Newman demonstrate how this misses the point. The real dispute was between two transnational coalitions--one favoring security, the other liberty--whose struggles have reshaped the politics of surveillance, e-commerce, and privacy rights. Looking at three large security debates in the period since 9/11, involving Passenger Name Record data, the SWIFT financial messaging controversy, and Edward Snowden's revelations, the authors examine how the powers of border-spanning coalitions have waxed and waned. Globalization has enabled new strategies of action, which security agencies, interior ministries, privacy NGOs, bureaucrats, and other actors exploit as circumstances dictate.
Book
A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications
Recent advances in deep learning have shown many successful stories in smart healthcare applications with data-driven insight into improving clinical institutions’ quality of care. Excellent deep learning models are heavily data-driven. The more data trained, the more robust and more generalizable the performance of the deep learning model. However, pooling the medical data into centralized storage to train a robust deep learning model faces privacy, ownership, and strict regulation challenges. Federated learning resolves the previous challenges with a shared global deep learning model using a central aggregator server. At the same time, patient data remain with the local party, maintaining data anonymity and security. In this study, first, we provide a comprehensive, up-to-date review of research employing federated learning in healthcare applications. Second, we evaluate a set of recent challenges from a data-centric perspective in federated learning, such as data partitioning characteristics, data distributions, data protection mechanisms, and benchmark datasets. Finally, we point out several potential challenges and future research directions in healthcare applications.
Journal Article
Protecting your privacy in a data-driven world
\"At what point does the sacrifice to our personal information outweigh the public good? If public policymakers had access to our personal and confidential data, they could make more evidence-based, data-informed decisions that could accelerate economic recovery and improve COVID-19 vaccine distribution. However, access to personal data comes at a steep privacy cost for contributors, especially underrepresented groups. Protecting Your Privacy in a Data-Driven World is a practical, nontechnical guide that explains the importance of balancing these competing needs and calls for careful consideration of how data are collected and disseminated by our government and the private sector. Not addressing these concerns can harm the same communities policymakers are trying to protect through data privacy and confidentiality legislation. Claire McKay Bowen is the Lead Data Scientist for Privacy and Data Security at the Urban Institute. Her research focuses on assessing the quality of differentially private data synthesis methods and science communication. In 2021, the Committee of Presidents of Statistical Societies identified her as an emerging leader in statistics for her technical contributions and leadership to statistics and the field of data privacy and confidentiality\"-- Provided by publisher.
Book
A Smart Contract-Based Dynamic Consent Management System for Personal Data Usage under GDPR
A massive amount of sensitive personal data is being collected and used by scientists, businesses, and governments. This has led to unprecedented threats to privacy rights and the security of personal data. There are few solutions that empower individuals to provide systematic consent agreements on distinct personal information and control who can collect, access, and use their data for specific purposes and periods. Individuals should be able to delegate consent rights, access consent-related information, and withdraw their given consent at any time. We propose a smart-contract-based dynamic consent management system, backed by blockchain technology, targeting personal data usage under the general data protection regulation. Our user-centric dynamic consent management system allows users to control their personal data collection and consent to its usage throughout the data lifecycle. Transaction history and logs are recorded in a blockchain that provides trusted tamper-proof data provenance, accountability, and traceability. A prototype of our system was designed and implemented to demonstrate its feasibility. The acceptability and reliability of the system were assessed by experimental testing and validation processes. We also analyzed the security and privacy of the system and evaluated its performance.
Journal Article
Practicing Privacy Literacy in Academic Libraries
Practicing Privacy Literacy in Academic Libraries: Theories, Methods, and Cases can help you teach privacy literacy, evolve the privacy practices at your institution, and re-center the individuals behind the data and the ethics behind library work.
eBook
Data privacy‐based coordinated placement method of workflows and data
With the rapid development of data acquisition technology, many industries data already have the characteristics of big data and cloud technology has provided strong support for the storage and complex calculations of these massive data. The meteorological department established the cloud data centre based on the existing storage and computing resources and re‐arranged the historical data to reduce the historical data access time of applications. However, the placement of each workflow and input data also affects the average data access time, which in turn affects the computing efficiency of the cloud data centre. At the same time, because of the collaborative processing of multiple nodes, the resource utilisation of cloud data centre has also been paid more and more attention. In addition, with the increase of data security requirements, some privacy conflict data should avoid being placed on the same or neighbouring nodes. In response to this challenge, based on the fat‐tree network topology, this study proposes a data privacy protection‐based collaborative placement strategy of workflow and data to jointly optimise the average data access time, the average resource utilisation, and the data conflict degree. Finally, a large number of experimental evaluations and comparative analyses verify the efficiency of the proposed method.
Journal Article