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[…] not implemented responsibility, and carefully controlled with both technical and legal safeguards, digital IDs might allow for increased location-tracking and user profiling, already a concern with cell phone technology. Blockchain technology, if not designed carefully, creates a public, immutable record of information exchanges, regarding where, when, and why a digital ID was requested. And […]

[…] “widely distributed media.” “Direct identifier” is defined as “information that is commonly used to identify a data subject, including name, physical address, email address, recognizable photograph, telephone number, and Social Security number.” “Deidentified data” is defined as “personal data that is modified to remove all direct identifiers and to reasonably ensure that the record […]

[…] of antitrust laws in nearly every sector of the economy. Although there is a focus on particular markets, such as agriculture and healthcare, the Order includes a number of provisions with clear implications for data protection and privacy.  In our view, the overarching theme of the Order is a concern with large platforms and […]

[…] qualitative test, the results shown by Carroll were quite different. When users were asked what they would do if they were offered a cookie banner, a significant number stated that they always rejected non-essential cookies. This demonstrates that, verbally, people were more willing to err on the side of privacy caution, which was not […]

[…] privacy laws, such as the California Consumer Privacy Act (CCPA), the Colorado Privacy Act, and the Virginia Consumer Data Protection Act. In addition, however, there are a number of other state privacy laws that can be considered “non-sectoral” because they apply broadly to businesses that collect or use personal information. These include, for example, […]

[…] rate, and other biomarkers, could detect COVID-19 infections prior to the onset of symptoms. To ensure the data sharing project minimized privacy risks, both teams took a number of steps including: Establishing limits on the sharing and use of personal health information. Using a researcher-friendly version of Empatica’s E4 device that prevents the collection […]

[…] study record IDs, were not shared with Empatica. Lessons for Future Data-Sharing Projects The data-sharing collaboration between the research team at Stanford Medicine and Empatica highlights a number of valuable lessons that companies and academic institutions may apply to future data-sharing collaborations. Work the Process. Empatica and the research team at Stanford Medicine established […]

[…] from Google users who had opted-in to share their data for research. Then they correlated the decreases in mobility tied to state-level policies with changes in the number of reported COVID-19 cases. The project produced the following insights: State-level emergency declarations resulted in a 9.9% reduction in time spent away from places of residence. […]

[…] infections prior to the onset of symptoms. To ensure that this data sharing project minimized potential privacy risks, both Empatica and the Stanford Medicine team took a number of steps, including:  Establishing limits on the sharing and use of personal health information. Using a researcher-friendly device, Empatica’s E4, that prevents the collection of geolocation […]

[…] like to integrate an AI component, that would be considered high-risk AI. Tielemans also noted that, once a system is qualified as high-risk, then providers acquire a number of obligations on training models, record keeping, among others.  On the DSA, Tielemans pointed out that the proposal is geared towards providers of online intermediary services. […]