InfoQ Homepage Privacy Content on InfoQ
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Privacy-First Re-Architecture
Nimisha Asthagiri discusses what it is like: an alternative architecture and ecosystem, where industry-wide decentralized data ownership is the prime directive.
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Perspectives on Trust in Security & Privacy
The panelists discuss balancing the adjustment of the security posture and the user experience.
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Privacy Architecture for Data-Driven Innovation
Nishant Bhajaria discusses how to set up a privacy program and shares tips on how to influence engineering and other teams to own their data and its usage so that privacy is a shared goal.
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The Internet of Things Might Have Less Internet Than We Thought?
Alasdair Allan looks at the possible implications of machine learning on the edge around privacy and security.
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Big Data Legal Issues. GDPR and Contracts
Anton Tarasiuk discusses the legal issues that can be encountered when dealing with Big Data, GDPR and contracts.
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Managing Privacy & Data Governance for Next Generation Architecture
Ayana Miller explores a governance framework for road mapping, resourcing, and driving decision-making for next generation of architecture with privacy by design.
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Mind the Software Gap: How We Can Operationalize Privacy & Compliance
Jean Yang talks about some of the ways GDPR and CCPA can influence software, but also about practical solutions to protecting data privacy and security.
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Users' Privacy Is in Your Hands!
Katarzyna Szymielewicz discusses technology and privacy, the need to consider privacy when designing systems, and the role of developers in this process.
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Privacy Tools and Techniques for Developers
Amber Welch talks about privacy engineering, from foundational principles to advanced techniques, as well as upcoming technologies like homomorphic encryption.
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Federated Learning: Rewards & Challenges of Distributed Private ML
Eric Tramel discusses the basic concepts underlying the federated ML approach, the advantages it brings, as well as the challenges associated with constructing federated solutions.
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Privacy: The Last Stand for Fair Algorithms
Katharine Jarmul discusses research related to fair-and-private ML algorithms and privacy-preserving models, showing that caring about privacy can help ensure a better model overall and support ethics
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Fairness, Transparency, and Privacy in AI @LinkedIn
Krishnaram Kenthapadi focuses on the application of privacy-preserving data mining and fairness-aware ML techniques in practice, by presenting case studies spanning different LinkedIn applications.
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