Project publications


  • Vizgirda et al. SocialGenPod: Privacy-Friendly Generative AI Social Web Applications with Decentralised Personal Data Stores. In WWW’2024. [pdf]
  • Zhao R and Zhao J. Perennial semantic data terms of use for decentralized web. In WWW’2024. [pdf]
  • Wang et al. KOALA Hero Toolkit: A New Approach to Inform Families of Mobile Datafication Risks. In CHI’2024. [pdf]
  • Wang et al. CHAITok: A Proof-of-Concept System Supporting Children’s Sense of Data Autonomy on Social Media. In CHI’2024. [pdf]
  • Wang et al. Challenges and opportunities in translating ethical AI principles into practice for children. Nature Machine Intelligence 6(3):265-270 20 Mar 2024. [pdf]


  • Ramesh et al. Decentralised, Scalable and Privacy-Preserving Synthetic Data Generation. [arxiv]
  • Zhao et al. Libertas: Privacy-Preserving Computation for Decentralised Personal Data Stores. [arxiv]
  • Zhao et al. Long-living Service for Cooperative Knowledge Use in Decentralized Data Stores. [arxiv]
  • Ekambaranathan A, Zhao J and Chalhoub G. ““Navigating the Data Avalanche: Towards Supporting Developers in Developing Privacy-Friendly Children’s Apps”. In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). [pdf]
  • Ekambaranathan A, Zhao J and Van Kleek M. “How Can We Design Privacy-Friendly Apps for Children? Using a Research through Design Process to Understand Developers’ Needs and Challenges”. CSCW 2023. [pdf]
  • Stein et al. “‘You are you and the app. There’s nobody else.’: Building Worker-Designed Data Institutions within Platform Hegemony.” Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 2023. [pdf]
  • Stein J, Jake ML and Calacci, Dan, “From access to understanding: Collective data governance for workers.” European Labor Law Journal. 2023. [pdf]
  • Wang et al. 12 Ways to Empower: Designing for Children’s Digital Autonomy. CHI 2023. [pdf]
  • Wang et al. “Treat me as your friend, not a number in your database”: Co-designing with Children to Cope with Datafication Online [pdf]
  • Zhao, J. Call for a new data governance structure for datafied childhood. [pdf]


  • Kollnig et al. Before and after GDPR: tracking in mobile apps. Internet Policy Review, 10(4).[pdf][Journal]
  • Wang et al. ‘Don’t make assumptions about me!’: Understanding Children’s Perception of Datafication Online. CSCW’22. [pdf][Journal]
  • Kollnig et al. Goodbye tracking? Impact of iOS app tracking transparency and privacy labels. In Proc. of FAccT’22 [pdf][Conference]
  • Wang et al. Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 536(1-29). pdf[presentation][Conference]
  • Veale et al. Fortifying the Algorithmic Management Provisions in the Proposed Platform Work Directive. [pdf][Report]
  • EWADA’s Response to DCMS Consultation on Online Safety Bill. June 2022. [pdf][Report]
  • A suits of essays published by our team members during our inaugural colloquium with the GoodEnough College in March 2022[link][Report]
  • EWADA’s Response to CMA Consultation on Mobile Ecosystems.February 2022[link][Report]


  • Wang et al. Protection or punishment? relating the design space of parental control apps and perceptions about them to support parenting for online safety. CSCW’21. [pdf][Journal]
  • Agrawal et al. Exploring design and governance challenges in the development of privacy-preserving computation. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 68 (1-3)[pdf][Conference]
  • Kollnig et al. Are iPhones Really Better for Privacy? Comparative Study of iOS and Android Apps. Proceedings on Privacy Enhancing Technologies. 2022. 2(6-24)[pdf][Conference]
  • EWADA’s Response to DCMS Consultation on Reformation of the UK’s Data Protection Regime. November 2021[pdf][Report]

Background publications

Theme 1: Architectures for Data Autonomy and Self-Determination

  • Binns et al. Third party tracking in the mobile ecosystem. In Proceedings of the 10th ACM Conference on Web Science, pages 23–31, 2018. pdf

  • Van Kleek et al. Iot refine: Making smart home devices accountable for their data harvesting practices. In Living in the Internet of Things: Cybersecurity of the IoT Conference. Living in the Internet of Things: Cybersecurity of the IoT Conference, 2019. pdf

  • Zhao et al. I make up a silly name’ understanding children’s perception of privacy risks online. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pages 1–13, 2019. pdf

Theme 2: Architectures for Data Privacy

  • Carissa Véliz. The internet and privacy. 2019. pdf

  • Agrawal et al. Quotient: two-party secure neural network training and prediction. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pages 1231–1247, 2019. pdf

Theme 3 Architectures for Accountability

  • Wachter et al. Why a right to explanation of automated decision- making does not exist in the general data protection regulation. International Data Privacy Law, 7(2):76–99, 2017. pdf

  • Wachter et al. Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harv. JL & Tech., 31:841, 2017. pdf

  • Binns et al. ’It’s reducing a human being to a percentage’ perceptions of justice in algorithmic decisions. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pages 1–14, 2018. pdf

  • Binns et al. Like trainer, like bot? inheritance of bias in algorithmic content moderation. In International Conference on Social Informatics, pages 405–415. Springer, 2017. pdf

  • Wachter and Mittelstadt. A right to reasonable inferences: re-thinking data protection law in the age of big data and AI. Colum. Bus. L. Rev., page 494, 2019.pdf

Theme 4 Architectures for Data Sharing

  • Carissa Véliz. Medical privacy and big data: A further reason in favour of public universal healthcare coverage. In: Philosophical Foundations of Medical Law. Oxford (UK): Oxford University Press; 2019. Wellcome Trust–Funded Monographs and Book Chapters.

  • Carissa Véliz. Three things digital ethics can learn from medical ethics. Nature Electronics, 2(8):316–318,