Information privacy law

Information privacy, data privacy or data protection laws provide a legal framework on how to obtain, use and store data of natural persons. The various laws around the world describe the rights of natural persons to control who is using its data. This includes usually the right to get details on which data is stored, for what purpose and to request the deletion in case the purpose is not given anymore. Over 80 countries and independent territories, including nearly every country in Europe and many in Latin America and the Caribbean, Asia, and Africa, have now adopted comprehensive data protection laws. The European Union has the General Data Protection Regulation (GDPR), in force since May 25, 2018. The United States is notable for not having adopted a comprehensive information privacy law, but rather having adopted limited sectoral laws in some areas like the California Consumer Privacy Act (CCPA). These laws are based on fair information practice guidelines developed by the U.S. Department for Health, Education and Welfare (HEW) (later renamed Department of Health & Human Services (HHS)), by a Special Advisory Committee on Automated Personal Data Systems, under the chairmanship of computer pioneer and privacy pioneer Willis H. Ware. The report submitted by the Chair to the HHS Secretary titled "Records, Computers and Rights of Citizens (07/01/1973)", proposes universal principles for the privacy and protection of consumer and citizen data: For all data collected, there should be a stated purpose. Information collected from an individual cannot be disclosed to other organizations or individuals unless specifically authorized by law or by consent of the individual. Records kept on an individual should be accurate and up to date. There should be mechanisms for individuals to review data about them, to ensure accuracy. This may include periodic reporting. Data should be deleted when it is no longer needed for the stated purpose. Transmission of personal information to locations where "equivalent" personal data protection cannot be assured is prohibited.

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency

Jean-Philippe Thiran, Tobias Kober, Bénédicte Marie Maréchal, Jonas Richiardi

With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a growing privacy concern. To protect subject privacy, several algorithms have ...

Wiley2024

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency

Bridging the gap between theoretical and practical privacy technologies for at-risk populations

Bridging the gap between theoretical and practical privacy technologies for at-risk populations

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning