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[…] explaining approaches to data protection. Unlike traditional software, in machine learning systems privacy or security harms do not necessarily require direct access to underlying data or source code. The whitepaper presents a layered approach to data protection in machine learning, including recommending techniques such as noise injection, inserting intermediaries between training data and the […]

[…] The GDPR is anything but a ‘notice and consent’ type of law. There are many reasons why this is the case, and I could go on and get lost into the minutiae of it. Instead, I’m listing 10 high level reasons, explained in plain language, to the best of my knowledge: 1. Data Protection […]

[…] open the submissions process for next year’s awards in fall 2020. Have you conducted privacy-related research that policymakers should know about? If so, we can help you get it in front of key government officials. The Future of Privacy Forum will return to Capitol Hill early next year (date TBD) for the 10th installment […]

[…] from the video file by those with the capacity to review it, and thus these types of fakes can be publicly identified.  When Machines Learn, the Fakes Get Smarter Two more recent versions of machine learning-based video editing are causing more concern. One is what is accurately a “deepfakes,” and the other, even newer […]

[…] Rather than risk copyright liability for displaying or altering content without a poster’s permission and expose themselves to statutory damages for copyright infringement, most websites prefer to get a license from their user that will be effective for as long as the business is active (perpetually), that will not require them to pay millions […]

[…] order (as with Facebook) or invoking specific statutes (such as the Children’s Online Privacy Protection Act). In fact, in many privacy cases the FTC has trouble even getting refunds for consumers. That’s because many companies provide online products and services for free – so it’s difficult to prove a financial loss. In those privacy […]

[…] of residents. For example, Quayside’s streets will prioritize transit, cycling, and walking instead of a car-centered design and the city will have a thermal grid for fossil- free heating and cooling. The plan also articulates inclusiveness for indigenous populations, individuals with disabilities, and other members of the community as a goal of the design.  […]

[…] does not take any of these approaches, but instead creates an “all or nothing” framework for data retention. Most consumers probably want something in between–the ability to get the benefits of voice personalization (for example, if they themselves have a strong accent or unusual speech pattern), and perhaps support product improvement, but with easier, […]

[…] and Innovation Foundation to undertake both projects. The Alliance will support data sharing efforts under way, help address and mitigate challenges that create barriers to sharing and promote practices that encourage more data sharing between industry and academic researchers. So far, more than 25 prominent companies are participating in the Alliance’s activities. In its […]

[…] not only to go beyond existing laws in addressing consumer privacy concerns, but also to help shape evolving legislative efforts. We are glad to see that the NAI is taking major steps in the right direction in order to continue to be on the front lines of protecting consumer privacy and promoting responsible business practices.