Privacy by design is an approach to systems engineering initially developed by Ann Cavoukian and formalized in a joint report on privacy-enhancing technologies by a joint team of the Information and Privacy Commissioner of Ontario (Canada), the Dutch Data Protection Authority, and the Netherlands Organisation for Applied Scientific Research in 1995. The privacy by design framework was published in 2009 and adopted by the International Assembly of Privacy Commissioners and Data Protection Authorities in 2010. Privacy by design calls for privacy to be taken into account throughout the whole engineering process. The concept is an example of value sensitive design, i.e., taking human values into account in a well-defined manner throughout the process.
Cavoukian's approach to privacy has been criticized as being vague, challenging to enforce its adoption, difficult to apply to certain disciplines, challenging to scale up to networked infrastructures, as well as prioritizing corporate interests over consumers' interests and placing insufficient emphasis on minimizing data collection. Recent developments in computer science and data engineering, such as support for encoding privacy in data and the availability and quality of Privacy-Enhancing Technologies (PET's) partly offset those critiques and help to make the principles feasible in real-world settings.
The European GDPR regulation incorporates privacy by design.
The privacy by design framework was developed by Ann Cavoukian, Information and Privacy Commissioner of Ontario, following her joint work with the Dutch Data Protection Authority and the Netherlands Organisation for Applied Scientific Research in 1995.
In 2009, the Information and Privacy Commissioner of Ontario co-hosted an event, Privacy by Design: The Definitive Workshop, with the Israeli Law, Information and Technology Authority at the 31st International Conference of Data Protection and Privacy Commissioner (2009).
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This advanced course will provide students with the knowledge to tackle the design of privacy-preserving ICT systems. Students will learn about existing technologies to prect privacy, and how to evalu
This course provides attendees with theoretical and practical issues in media security. In addition to lectures by the professor, the course includes laboratory sessions, a mini-project, and a mid-ter
This is an introductory course to computer security and privacy. Its goal is to provide students with means to reason about security and privacy problems, and provide them with tools to confront them.
The first MOOC about responsible use of technology for humanitarians. Learn about technology and identify risks and opportunities when designing digital solutions.
Covers the principles and strategies of privacy engineering, emphasizing the importance of embedding privacy into IT systems and the challenges faced in achieving privacy by design.
Privacy-enhancing technologies (PET) are technologies that embody fundamental data protection principles by minimizing personal data use, maximizing data security, and empowering individuals. PETs allow online users to protect the privacy of their personally identifiable information (PII), which is often provided to and handled by services or applications. PETs use techniques to minimize an information system's possession of personal data without losing functionality.
Privacy (UK, US) is the ability of an individual or group to seclude themselves or information about themselves, and thereby express themselves selectively. The domain of privacy partially overlaps with security, which can include the concepts of appropriate use and protection of information. Privacy may also take the form of bodily integrity. There have been many different conceptions of privacy throughout history. Most cultures recognize the right of an individual to withhold aspects of their personal lives from public record.
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Distributed constraint optimization (DCOP) is a framework in which multiple agents with private constraints (or preferences) cooperate to achieve a common goal optimally. DCOPs are applicable in several multi-agent coordination/allocation problems, such as ...
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Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...
In contrast to vast academic efforts to study AI security, few real-world reports of AI security incidents exist. Released incidents prevent a thorough investigation of the attackers' motives, as crucial information about the company and AI application is ...