InfoQ Homepage Privacy Content on InfoQ
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Busting AI Myths and Embracing Realities in Privacy & Security
Katharine Jarmul keynotes on common myths around privacy and security in AI and explores what the realities are, covering design patterns that help build more secure, more private AI systems.
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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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