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[…] must also appoint Data Protection Officers (DPOs) in specific situations, depending on the volume of PI processed, and conduct regular compliance training. Individuals are granted an extensive number of “rights in personal information handling activities”. The PIPL provides for individual rights very similar to GDPR’s “rights of the data subject”, such as erasure and […]

[…] advent of unprecedentedly large datasets, increased computational power, advances in data science, machine learning, and statistical modeling. AI models include programming and system design based on a number of sub-categories, such as robotics, expert systems, scheduling and planning systems, natural language processing, neural networks, computer sensing, and machine learning. In many cases of consumer […]

[…] 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. […]