Information privacy is the relationship between the collection and dissemination of data, technology, the public expectation of privacy, contextual information norms, and the legal and political issues surrounding them. It is also known as data privacy or data protection.
Data privacy is challenging since attempts to use data while protecting an individual's privacy preferences and personally identifiable information. The fields of computer security, data security, and information security all design and use software, hardware, and human resources to address this issue.
Various types of personal information often come under privacy concerns.
This describes the ability to control what information one reveals about oneself over cable television, and who can access that information. For example, third parties can track IP TV programs someone has watched at any given time. "The addition of any information in a broadcasting stream is not required for an audience rating survey, additional devices are not requested to be installed in the houses of viewers or listeners, and without the necessity of their cooperations, audience ratings can be automatically performed in real-time."
In the United Kingdom in 2012, the Education Secretary Michael Gove described the National Pupil Database as a "rich dataset" whose value could be "maximised" by making it more openly accessible, including to private companies. Kelly Fiveash of The Register said that this could mean "a child's school life including exam results, attendance, teacher assessments and even characteristics" could be available, with third-party organizations being responsible for anonymizing any publications themselves, rather than the data being anonymized by the government before being handed over. An example of a data request that Gove indicated had been rejected in the past, but might be possible under an improved version of privacy regulations, was for "analysis on sexual exploitation".
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The first MOOC about responsible use of technology for humanitarians. Learn about technology and identify risks and opportunities when designing digital solutions.
In this class we will explore some of the fundamental ways in which the pervasiveness of digital devices has completely revolutionized the world of music in the last 40 years, both from the point of v
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
The General Data Protection Regulation (Regulation (EU) 2016/679, abbreviated GDPR) is a European Union regulation on Information privacy in the European Union (EU) and the European Economic Area (EEA). The GDPR is an important component of EU privacy law and human rights law, in particular Article 8(1) of the Charter of Fundamental Rights of the European Union. It also governs the transfer of personal data outside the EU and EEA.
Tor, short for The Onion Router, is free and open-source software for enabling anonymous communication. It directs Internet traffic via a free, worldwide, volunteer overlay network that consists of more than seven thousand relays. Using Tor makes it more difficult to trace a user's Internet activity. Tor protects personal privacy by concealing a user's location and usage from anyone performing network surveillance or traffic analysis. It protects the user's freedom and ability to communicate confidentially through IP address anonymity using Tor exit nodes.
Privacy law is the body of law that deals with the regulating, storing, and using of personally identifiable information, personal healthcare information, and financial information of individuals, which can be collected by governments, public or private organisations, or other individuals. It also applies in the commercial sector to things like trade secrets and the liability that directors, officers, and employees have when handing sensitive information.
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 ...
EPFL2024
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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 ...
Dordrecht2024
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Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training. Practical DP-ensuring training methods use randomization when fitting model parameters to privacy-sensitive data (e.g., adding Ga ...