Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
Gemini 2.5 Pro
GPT-5
GPT-4o
DeepSeek R1 via Azure
2000 character limit reached

Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence (2301.05995v2)

Published 15 Jan 2023 in cs.AI, cs.CY, cs.DC, cs.IR, and cs.MA

Abstract: Collective privacy loss becomes a colossal problem, an emergency for personal freedoms and democracy. But, are we prepared to handle personal data as scarce resource and collectively share data under the doctrine: as little as possible, as much as necessary? We hypothesize a significant privacy recovery if a population of individuals, the data collective, coordinates to share minimum data for running online services with the required quality. Here we show how to automate and scale-up complex collective arrangements for privacy recovery using decentralized artificial intelligence. For this, we compare for first time attitudinal, intrinsic, rewarded and coordinated data sharing in a rigorous living-lab experiment of high realism involving >27,000 real data disclosures. Using causal inference and cluster analysis, we differentiate criteria predicting privacy and five key data-sharing behaviors. Strikingly, data-sharing coordination proves to be a win-win for all: remarkable privacy recovery for people with evident costs reduction for service providers.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (66)
  1. Temporal and cultural limits of privacy in smartphone app usage. Scientific reports 11, 1–9 (2021).
  2. Unique in the crowd: The privacy bounds of human mobility. Scientific reports 3, 1–5 (2013).
  3. Modelling imperfect knowledge via location semantics for realistic privacy risks estimation in trajectory data. Scientific reports 12, 1–10 (2022).
  4. An empirical approach to understanding privacy valuation. HBS marketing research paper (2007).
  5. Selling privacy at auction. Games and Economic Behavior 91, 334–346 (2015).
  6. Sweeney, L. k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, 557–570 (2002).
  7. What B2B Can Learn from B2C About Data Privacy and Sharing. URL https://www.bcg.com/en-gb/publications/2020/imperative-of-data-privacy-plans-for-b2b-companies-part-4.
  8. Privacy and human behavior in the age of information. Science 347, 509–514 (2015).
  9. Conservative or liberal? personalized differential privacy. In Proceedings of the 31st International Conference on Data Engineering, 1023–1034 (IEEE, 2015).
  10. Optimization of privacy-utility trade-offs under informational self-determination. Future Generation Computer Systems 109, 488–499 (2020).
  11. A pragmatic introduction to secure multi-party computation. Foundations and Trends® in Privacy and Security 2, 70–246 (2018).
  12. On the complexity of optimal k-anonymity. In Proceedings of the 23rd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of database systems, 223–228 (2004).
  13. The value of our digital identity. URL https://www.bcg.com/en-gb/publications/2012/digital-economy-consumer-insight-value-of-our-digital-identity.
  14. Data Privacy by the Numbers, BCG Global Consumer Sentiment Survey. URL https://www.bcg.com/en-gb/publications/2014/data-privacy-numbers.
  15. What is privacy worth? The Journal of Legal Studies 42, 249–274 (2013).
  16. Disclosing information about the self is intrinsically rewarding. Proceedings of the National Academy of Sciences 109, 8038–8043 (2012).
  17. Unwillingness to pay for privacy: A field experiment. Economics Letters 117, 25–27 (2012).
  18. The global landscape of AI ethics guidelines. Nature Machine Intelligence 1, 389–399 (2019).
  19. Jones, N. How to stop data centres from gobbling up the world’s electricity. Nature 561, 163–167 (2018).
  20. Protecting elections from social media manipulation. Science 365, 858–861 (2019).
  21. Oulasvirta, A. et al. Long-term effects of ubiquitous surveillance in the home. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing, 41–50 (2012).
  22. Artificial intelligence: risks to privacy and democracy. Yale JL & Tech. 21, 106 (2019).
  23. Too much choice: End-user privacy decisions in the context of choice proliferation. In Symposium on Usable Privacy and Security (SOUPS), 69–87 (2014).
  24. Dafoe, A. et al. Cooperative AI: machines must learn to find common ground. Nature 593 (2021).
  25. Decentralized collective learning for self-managed sharing economies. ACM Transactions on Autonomous and Adaptive Systems (TAAS) 13, 1–33 (2018).
  26. Lippi, M. et al. Consumer protection requires artificial intelligence. Nature machine intelligence 1, 168–169 (2019).
  27. Joshi, K. P. et al. Alda: Cognitive assistant for legal document analytics. In 2016 AAAI Fall Symposium Series (2016).
  28. Personalized privacy assistants for the internet of things: Providing users with notice and choice. IEEE Pervasive Computing 17, 35–46 (2018).
  29. Collective learning: Unesco ircai global top 100 outstanding ai projects tackling sustainable development goals. URL https://ircai.org/presenting-the-global-top-100-outstanding-projects-collective-learning/.
  30. Solving artificial intelligence’s privacy problem. Field Actions Science Reports 17, 80–83 (2017).
  31. Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence 2, 305–311 (2020).
  32. A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion. Information Fusion 51, 129–144 (2019).
  33. Enhancing privacy in robotics via judicious sensor selection. In Proceedings of the International Conference on Robotics and Automation, 7156–7165 (IEEE, 2020).
  34. An adaptive sensor selection framework for multisensor prognostics. Journal of Quality Technology 1–20 (2021).
  35. A new coordination approach to mitigate composition attack. In Proceedings of the International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), 274–279 (IEEE, 2021).
  36. Improving app privacy: Nudging app developers to protect user privacy. IEEE Security & Privacy 12, 55–58 (2014).
  37. What’s the value of your privacy? Exploring factors that influence privacy-sensitive contributions to participatory sensing applications. In Proceedings of the 38th Workshop on Local Computer Networks, 918–923 (IEEE, 2013).
  38. The privacy paradox: Personal information disclosure intentions versus behaviors. Journal of consumer affairs 41, 100–126 (2007).
  39. Privacy in multimedia communications: Protecting users, not just data. In People and computers XV—Interaction without frontiers, 49–64 (Springer, 2001).
  40. Collective exposure: Peer effects in voluntary disclosure of personal data. In International conference on financial cryptography and data security, 1–15 (Springer, 2011).
  41. The hidden cost of accommodating crowdfunder privacy preferences: A randomized field experiment. Management Science 61, 949–962 (2015).
  42. The effect of online privacy information on purchasing behavior: An experimental study. Information systems research 22, 254–268 (2011).
  43. Ballandies, M. C. To incentivize or not: Impact of blockchain-based cryptoeconomic tokens on human information sharing behavior. IEEE Access 10, 74111–74130 (2022).
  44. Silent listeners: The evolution of privacy and disclosure on facebook. Journal of privacy and confidentiality 4, 2 (2013).
  45. Véliz, C. Privacy is Power: Why and how You Should Take Back Control of Your Data (Bantam Press, 2020). URL https://books.google.co.uk/books?id=c8ZWzQEACAAJ.
  46. Lawson, J. et al. The data use ontology to streamline responsible access to human biomedical datasets. Cell Genomics 1, 100028 (2021).
  47. Morozov, E. Digital socialism? the calculation debate in the age of big data. New Left Review 33–67 (2019).
  48. Muldoon, J. Data-owning democracy or digital socialism? Critical Review of International Social and Political Philosophy 1–22 (2022).
  49. Fischli, R. Data-owning democracy: Citizen empowerment through data ownership. European Journal of Political Theory (2022).
  50. Peer to Peer (University of Westminster Press, London, 2019).
  51. How value-sensitive design can empower sustainable consumption. Royal Society open science 8, 201418 (2021).
  52. Socio-technical smart grid optimization via decentralized charge control of electric vehicles. Applied soft computing 82, 105573 (2019).
  53. ETH Zurich Decision Science Lab. URL https://www.descil.ethz.ch.
  54. Pool of participants for the DeSciL lab. URL https://www.descil.ethz.ch/lab/participants.
  55. A study of age and gender seen through mobile phone usage patterns in mexico. In Proceedings of the International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 836–843 (IEEE, 2014).
  56. Mobile phone and young people. a survey pilot study to explore the controversial aspects of a new social phenomenon. Minerva pediatrica 58, 357—363 (2006).
  57. Comparing internet and mobile phone usage: digital divides of usage, adoption, and dropouts. Telecommunications Policy 27, 597–623 (2003).
  58. Evaluating the privacy properties of telephone metadata. Proceedings of the National Academy of Sciences 113, 5536–5541 (2016).
  59. Holarchic structures for decentralized deep learning: a performance analysis. Cluster Computing 23, 219–240 (2020).
  60. Jain, A. K. Data clustering: 50 years beyond k-means. Pattern recognition letters 31, 651–666 (2010).
  61. Fast R functions for robust correlations and hierarchical clustering. Journal of statistical software 46 (2012).
  62. Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion? Journal of classification 31, 274–295 (2014).
  63. Finding groups in data: an introduction to cluster analysis, vol. 344 (John Wiley & Sons, 2009).
  64. Hennig, C. Cluster-wise assessment of cluster stability. Computational Statistics & Data Analysis 52, 258–271 (2007).
  65. Rousseeuw, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics 20, 53–65 (1987).
  66. The application of k-medoids and pam to the clustering of rules. In International Conference on Intelligent Data Engineering and Automated Learning, 173–178 (Springer, 2004).
Citations (7)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now