• Khalid U. Fallatah
  • Mahmoud Barhamgi
  • Charith Perera


Internet services have collected our personal data since their inception. In the beginning, the personal data collection was uncoordinated and was limited to a few selected data types such as names, ages, birthdays, etc. Due to the widespread use of social media, more and more personal data has been collected by different online services. We increasingly see that Internet of Things (IoT) devices are also being adopted by consumers, making it possible for companies to capture personal data (including very sensitive data) with much less effort and autonomously at a very low cost. Current systems architectures aim to collect, store, and process our personal data in the cloud with very limited control when it comes to giving back to citizens. However, Personal Data Stores (PDS) have been proposed as an alternative architecture where personal data will be stored within households, giving us complete control (self-sovereignty) over our data. This paper surveys the current literature on Personal Data Stores (PDS) that enable individuals to collect, control, store, and manage their data. In particular, we provide a comprehensive review of related concepts and the expected benefits of PDS platforms. Further, we compare and analyse existing PDS platforms in terms of their capabilities and core components. Subsequently, we summarise the major challenges and issues facing PDS platforms’ development and widespread adoption.


  1. SimonKemp. Digital 2021: Global Overview Report. DataReportal, Global Digital Insights. Available online: (accessed on 15 November 2022).
  2. Rose, K.; Eldridge, S.; Chapin, L. The Internet of Things (IoT): An Overview. Int. J. Eng. Res. Appl. 2015, 5, 71–82. [Google Scholar]
  3. Hummel, P.; Braun, M.; Dabrock, P. Own Data? Ethical Reflections on Data Ownership. Philos. Technol. 2021, 34, 545–572. [Google Scholar] [CrossRef]
  4. Alessi, M.; Camillò, A.; Giangreco, E.; Matera, M.; Pino, S.; Storelli, D. A decentralized personal data store based on ethereum: Towards GDPR compliance. J. Commun. Softw. Syst. 2019, 15, 79–88. [Google Scholar] [CrossRef]
  5. Brochot, G.; Brunini, J.; Eisma, F.; Larsen, R.; Lewis, D. Study on Personal Data Stores Conducted; The Cambridge University Judge Business School: Cambridge, UK, 2015; pp. 458–459. [Google Scholar]
  6. Lulandala, E.E. Facebook Data Breach: A Systematic Review of Its Consequences on Consumers’ Behaviour towards Advertising; Springer Nature Singapore Pte Ltd.: Singapore, 2020; pp. 45–68. [Google Scholar] [CrossRef]
  7. Moiso, C.; Minerva, R. Towards a user-centric personal data ecosystem the role of the bank of individuals’ data. In Proceedings of the 2012 16th International Conference on Intelligence in Next Generation Networks, ICIN 2012, Berlin, Germany, 8–11 October 2012; pp. 202–209. [Google Scholar] [CrossRef]
  8. Shanmugarasa, Y.; Paik, H.Y.; Kanhere, S.S.; Zhu, L. Towards Automated Data Sharing in Personal Data Stores. In Proceedings of the 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events, PerCom Workshops 2021, Kassel, Germany, 22–26 March 2021; pp. 328–331. [Google Scholar] [CrossRef]
  9. Kongruangkit, S.; Xia, Y.; Xu, X.; Paik, H.Y. A case for connecting SOLiD and blockchains: Enforcement of transparent access rights in personal data stores. In Proceedings of the IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2021, Sydney, Australia, 3–6 May 2021. [Google Scholar] [CrossRef]
  10. Cai, T.; Hong, Z.; Liu, S.; Chen, W.; Zheng, Z.; Yu, Y. SocialChain: Decoupling Social Data and Applications to Return Your Data Ownership. IEEE Trans. Serv. Comput. 2021, 16, 600–614. [Google Scholar] [CrossRef]
  11. Singh, B.C.; Carminati, B.; Ferrari, E. Privacy-Aware Personal Data Storage (P-PDS): Learning how to Protect User Privacy from External Applications. IEEE Trans. Dependable Secur. Comput. 2021, 18, 889–903. [Google Scholar] [CrossRef]
  12. Mishra, N.; Levkowitz, H. PDV: Permissioned Blockchain based Personal Data Vault using Predictive Prefetching. In Proceedings of the ACM International Conference Proceeding Series, Ho Chi Minh City, Vietnam, 8–10 July 2021; pp. 59–69. [Google Scholar] [CrossRef]
  13. World Economic Forum. Rethinking Personal Data: Trust and Context in User-Centred Data Ecosystems; Technical Report May; World Economic Forum: Geneva, Switzerland, 2014. [Google Scholar]
  14. Van Kleek, M.; OHara, K. The Future of Social Is Personal: The Potential of the Personal Data Store; Social Collective Intelligence; Springer: Cham, Switzerland, 2014; pp. 125–158. [Google Scholar] [CrossRef][Green Version]
  15. Schwab, K.; Marcus, A.; Oyola, J.O.; Hoffman, W.; Michele, L. Personal Data: The Emergence of a New Asset Class. 2011; pp. 1–40. Available online: (accessed on 15 November 2022).
  16. Wang, J.; Wang, Z. A Survey on Personal Data Cloud. Sci. World J. 2014, 2014, 13. [Google Scholar] [CrossRef][Green Version]
  17. Kotut, L.; Horning, M.; Stelter, T.L.; Scott McCrickard, D. Willing buyer, willing Seller: Personal data trade as a service. In Proceedings of the International ACM SIGGROUP Conference on Supporting Group Work, Sanibel Island, FL, USA, 6–8 January 2020; pp. 59–68. [Google Scholar] [CrossRef]
  18. Cao, X.; Chen, Y.; Ray Liu, K.J. Data Trading with Multiple Owners, Collectors, and Users: An Iterative Auction Mechanism. IEEE Trans. Signal and Inf. Process. Netw. 2017, 3, 268–281. [Google Scholar] [CrossRef]
  19. Perera, C.; Wakenshaw, S.Y.; Baarslag, T.; Haddadi, H.; Bandara, A.K.; Mortier, R.; Crabtree, A.; Ng, I.C.; McAuley, D.; Crowcroft, J. Valorising the IoT Databox: Creating value for everyone. Trans. Emerg. Telecommun. Technol. 2017, 28, e3125. [Google Scholar] [CrossRef]
  20. Moiso, C.; Antonelli, F.; Vescovi, M. How do I manage my personal data?—A telco perspective. In Proceedings of the DATA 2012—Proceedings of the International Conference on Data Technologies and Applications, Rome, Italy, 25–27 July 2012; pp. 123–128. [Google Scholar] [CrossRef]
  21. Chessa, M.; Loiseau, P. CPDS: The Cooperative Personal Data Store for managing social network data. Accessed on 2015, 30, 2018. [Google Scholar]
  22. Haberer, B.; Kraemer, J.; Schnurr, D. Standing on the Shoulders of Web Giants: The Economic Effects of Personal Data Brokers. SSRN Electron. J. 2020, 1–60. [Google Scholar] [CrossRef]
  23. Teraoka, T. A study of exploration of heterogeneous per- sonal data collected from mobile devices and web services. In Proceedings of the 5th FTRA International Conference on Multimedia andUbiquitous Engineering(MUE ’11), Loutraki, Greece, 28–30 June 2011; pp. 239–245. [Google Scholar]
  24. Janssen, H.; Cobbe, J.; Norval, C.; Singh, J. Decentralised Data Processing: Personal Data Stores and the GDPR. SSRN Electron. J. 2020, 10, 356–384. [Google Scholar] [CrossRef]
  25. Perera, C.; Zaslavsky, A.; Christen, P.; Georgakopoulos, D. Context aware computing for the internet of things: A survey. IEEE Commun. Surv. Tutor. 2014, 16, 414–454. [Google Scholar] [CrossRef][Green Version]
  26. De Montjoye, Y.A.; Shmueli, E.; Wang, S.S.; Pentland, A.S. OpenPDS: Protecting the privacy of metadata through SafeAnswers. PLoS ONE 2014, 9, e98790. [Google Scholar] [CrossRef] [PubMed][Green Version]
  27. Mortier, R.; Zhao, J.; Crowcroft, J.; Wang, L.; Li, Q.; Haddadi, H.; Amar, Y.; Crabtree, A.; Colley, J.; Lodge, T.; et al. Personal data management with the databox: What’s inside the box? In Proceedings of the CAN 2016—2016 ACM Workshop on Cloud-Assisted Networking, co-located with CoNEXT 2016, Irvine, CA, USA, 12 December 2016; pp. 49–54. [Google Scholar] [CrossRef]
  28. Vescovi, M.; Moiso, C.; Pasolli, M.; Cordin, L.; Antonelli, F. Building an eco-system of trusted services via user control and transparency on personal data. IFIP Adv. Inf. Commun. Technol. 2015, 454, 240–250. [Google Scholar] [CrossRef][Green Version]
  29. Elsden, C.; Kirk, D.; Selby, M.; Speed, C. Beyond personal informatics: Designing for experiences with data. Conf. Hum. Factors Comput. Syst. 2015, 18, 2341–2344. [Google Scholar] [CrossRef]
  30. Ohlin, F.; Olsson, C.M. Beyond a utility view of personal informatics: A postphenomenological framework. In Proceedings of the UbiComp and ISWC 2015, Osaka, Japan, 7–11 September 2015; pp. 1087–1092. [Google Scholar] [CrossRef]
  31. Van Kleunen, L.; Voida, S. Challenges in supporting social practices around personal data for long-term mental health management. In Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, London, UK, 9–13 September 2019; pp. 944–948. [Google Scholar] [CrossRef]
  32. Epstein, D.A. Personal informatics in everyday life. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the Proceedings of the 2015 ACM International Symposium on Wearable Computers, Osaka, Japan, 7–11 September 2015; pp. 429–434. [Google Scholar] [CrossRef]
  33. Jones, W.; Bruce, H.; Bates, M.J.; Belkin, N.; Bergman, O.; Marshall, C. Personal information management in the present and future perfect: Reports from a special NSF-sponsored workshop. Proc. Am. Soc. Inf. Sci. Technol. 2006, 42, 2005. [Google Scholar] [CrossRef]
  34. Anciaux, N.; Bonnet, P.; Bouganim, L.; Nguyen, B.; Pucheral, P.; Sandu Popa, I.; Scerri, G. Personal Data Management Systems: The security and functionality standpoint. Inf. Syst. 2019, 80, 13–35. [Google Scholar] [CrossRef][Green Version]
  35. Loudet, J.; Sandu-Popa, I.; Bouganim, L. DISPERS: Securing highly distributed queries on personal data management systems. Proc. VLDB Endow. 2018, 12, 1886–1889. [Google Scholar] [CrossRef]
  36. Bus, J.; Nguyen, C. Personal Data Management—A Structured Discussion. In Digital Enlightenment Yearbook 2013; IOS Press: Amsterdam, The Netherlands; Berlin, Germany; Tokyo, Japan; Washington, DC, USA, 2013; pp. 270–287. [Google Scholar] [CrossRef]
  37. Rosner, G. Who owns your data? In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Seattle, WA, USA, 13–17 September 2014; pp. 623–628. [Google Scholar] [CrossRef]
  38. Chaudhry, A.; Crowcroft, J.; Howard, H.; Madhavapeddy, A.; Mortier, R.; Haddadi, H.; McAuley, D. Personal Data: Thinking Inside the Box. Aarhus Ser. Hum. Centered Comput. 2015, 1, 4. [Google Scholar] [CrossRef][Green Version]
  39. Yadav, P.; Moore, J.; Li, Q.; Mortier, R.; Amar, Y.; Shamsabadi, A.S.; Brown, A.; Crabtree, A.; Greenhalgh, C.; McAuley, D.; et al. Providing occupancy as a service with databox. In Proceedings of the 1st Workshop on Smart Cities and Fog Computing, Part of SenSys 2018, Shenzhen, China, 4 November 2018; pp. 29–34. [Google Scholar] [CrossRef]
  40. Amar, Y.; Haddadi, H.; Mortier, R. Privacy-Aware Infrastructure for Managing Personal Data Personal Data Arbitering within the Databox Framework. In Proceedings of the 2016 conference on ACM SIGCOMM 2016 Conference, Florianopolis, Brazil, 22–26 August 2016; pp. 571–572. [Google Scholar]
  41. Vilaza, G.N.; Bardram, J.E. Sharing access to behavioural and personal health data: Designers’ perspectives on opportunities and barriers. In Proceedings of the ACM International Conference Proceeding Series, Guilin, China, 16–17 November 2019; pp. 346–350. [Google Scholar] [CrossRef]
  42. Crabtree, A.; Lodge, T.; Colley, J.; Greenhalgh, C.; Glover, K.; Haddadi, H.; Amar, Y.; Mortier, R.; Li, Q.; Moore, J.; et al. Building accountability into the Internet of Things: The IoT Databox model. J. Reliab. Intell. Environ. 2018, 4, 39–55. [Google Scholar] [CrossRef][Green Version]
  43. Zyskind, G.; Nathan, O.; Pentland, A.S. Decentralizing privacy: Using blockchain to protect personal data. In Proceedings of the 2015 IEEE Security and Privacy Workshops, SPW 2015, San Jose, CA, USA, 21–22 May 2015; pp. 180–184. [Google Scholar] [CrossRef]
  44. Chowdhury, M.J.M.; Colman, A.; Han, J.; Kabir, M.A. A system architecture for subject-centric data sharing. In Proceedings of the Australasian Computer Science Week Multiconference, Brisband, QLD, Australia, 29 January–2 February 2018. [Google Scholar] [CrossRef]
  45. Pasquier, T.; Eyers, D.; Bacon, J. Viewpoint personal data and the internet of things. Commun. ACM 2019, 62, 32–34. [Google Scholar] [CrossRef][Green Version]
  46. Muñoz-Fernández, J.C.; Tamura, G.; Villegas, N.M.; Hausi, A.M. Surprise: User-Controlled Granular Privacy and Security for Personal Data in SmarterContext. In Proceedings of the 2012 Conference of the Center for Advanced Studies on Collaborative Research, Toronto, Canada, 5–7 November 2012; pp. 131–145. [Google Scholar]
  47. Langendoerfer, P.; Maaser, M. Privacy Guaranteeing Execution Containers: One time use of personal data by location based services. In Accociation Computing Machinery; Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering: Brussels, Belgium, 2010; pp. 1–6. [Google Scholar] [CrossRef]
  48. Bell, G. A personal digital store. Commun. ACM 2001, 44, 86–91. [Google Scholar] [CrossRef]
  49. Gemmell, J.; Bell, G.; Jaimetee-Van, L.; Williamjone, S.; Benjamin, R. MyLifeBits: A personal database for everything. Commun. ACM 2006, 49, 88–95. [Google Scholar] [CrossRef]
  50. Van Kleek, M.; Smith, D.A.; Tinati, R.; O’Hara, K.; Hall, W.; Shadbolt, N. 7 billion home telescopes: Observing social machines through personal data stores. In Proceedings of the 23rd International Conference on World Wide Web, Seoul, Republic of Korea, 7–11 April 2014; pp. 915–920. [Google Scholar] [CrossRef][Green Version]
  51. Li, Y.; Meng, X. Research on personal dataspace management. In Proceedings of the 2nd SIGMOD PhD Workshop on Innovative Database Research, IDAR, Vancouver, BC, Canada, 13 June 2008; pp. 7–12. [Google Scholar] [CrossRef]
  52. Vitale, F.; Odom, W.; McGrenere, J. Keeping and discarding personal data: Exploring a design space. In Proceedings of the 2019 ACM Designing Interactive Systems Conference, San Diego, CA, USA, 23–28 June 2019; pp. 1463–1477. [Google Scholar] [CrossRef]
  53. Van Kleek, M.; Smith, D.A.; Murray-Rust, D.; Guy, A.; O’Hara, K.; Dragan, L.; Shadbolt, N.R. Social personal data stores: The nuclei of decentralised social machines. In Proceedings of the 24th International Conference on World Wide Web, Florence, Italy, 18–22 May 2015; pp. 1155–1160. [Google Scholar] [CrossRef][Green Version]
  54. Perentis, C.; Vescovi, M.; Leonardi, C.; Moiso, C.; Musolesi, M.; Pianesi, F.; Lepri, B. Anonymous or not? Understanding the factors affecting personal mobile data disclosure. ACM Trans. Internet Technol. 2017, 17, 1–19. [Google Scholar] [CrossRef][Green Version]
  55. Duisberg, A. Legal Aspects of IDS: Data Sovereignty—What Does It Imply? In Designing Data Spaces; Springer Nature Switzerland AG: Cham, Switzerland, 2022; pp. 61–90. [Google Scholar] [CrossRef]
  56. Hummel, P.; Braun, M.; Augsberg, S.; Dabrock, P. Sovereignty and data sharing. ITU J. ICT Discov. Spec. Issue 2018, 25, 1–10. [Google Scholar]
  57. Scerri, S.; Augustin, S. Industrial Data Space—Digital Sovereignty over Data. In Proceedings of the Digitising European Industry WG2 Meeting, Brussels, Belgium, 8 December 2016. [Google Scholar]
  58. Ctrl SHIFT. The New Personal Data Landscape; Technical Report. 2011. Available online: (accessed on 23 March 2021).
  59. Ohlin, F.; Olsson, C.M. Intelligent computing in personal informatics: Key design considerations. In Proceedings of the International Conference on Intelligent User Interfaces, Proceedings IUI, Atlanta, GA, USA, 29 March–1 April 2015; pp. 263–274. [Google Scholar] [CrossRef]
  60. Li, I.; Dey, A.; Forlizzi, J. A stage-based model of personal informatics systems. Conf. Hum. Factors Comput. Syst. 2010, 1, 557–566. [Google Scholar] [CrossRef]
  61. Kurze, A.; Bischof, A.; Totzauer, S.; Storz, M.; Eibl, M.; Brereton, M.; Berger, A. Guess The Data: Data Work To Understand How People Make Sense Of And Use Simple Sensor Data From Homes. In Proceedings of the CHI ’20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, 25–30 April 2020; pp. 1–12. [Google Scholar] [CrossRef]
  62. Graham, L.; Tang, A.; Neustaedter, C. Help me help you: Shared reflection for personal data. In Proceedings of the International ACM SIGGROUP Conference on Supporting Group Work, Sanibel Island, FL, USA, 13–16 November 2016; pp. 99–109. [Google Scholar] [CrossRef]
  63. Choe, E.K.; Lee, B.; Zhu, H.; Riche, N.H. Understanding self-reflection: How people reflect on personal data through visual data exploration. In Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare, Barcelona, Spain, 23–26 May 2017; pp. 173–182. [Google Scholar] [CrossRef]
  64. Feustel, C.; Aggarwal, S.; Lee, B.; Wilcox, L. People Like Me: Designing for Reflection on Aggregate Cohort Data in Personal Informatics Systems. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2018, 2, 1–21. [Google Scholar] [CrossRef]
  65. Zheng, S.; Zhang, Q.; Zheng, R.; Huang, B.Q.; Song, Y.L.; Chen, X.C. Combining a multi-agent system and communication middleware for smart home control: A universal control platform architecture. Sensors 2017, 17, 2135. [Google Scholar] [CrossRef][Green Version]
  66. Javed, A.; Malhi, A.; Kinnunen, T.; Framling, K. Scalable IoT Platform for Heterogeneous Devices in Smart Environments. IEEE Access 2020, 8, 211973–211985. [Google Scholar] [CrossRef]
  67. Kim, S.; Park, M.; Lee, S.; Kim, J. Smart home forensics—Data analysis of iot devices. Electronics 2020, 9, 1215. [Google Scholar] [CrossRef]
  68. Wang, P.; Ye, F.; Chen, X. A Smart Home Gateway Platform for Data Collection and Awareness. IEEE Commun. Mag. 2018, 56, 87–93. [Google Scholar] [CrossRef][Green Version]
  69. Kafle, K.; Moran, K.; Manandhar, S.; Nadkarni, A.; Poshyvanyk, D. A study of data store-based home automation. In Proceedings of the 9th ACM Conference on Data and Application Security and Privacy, Richardson, TX, USA, 25–27 March 2019; pp. 73–84. [Google Scholar] [CrossRef][Green Version]
  70. Wang, H.; Yuan, Y.; Yang, F. A personal data determination method based on blockchain technology and smart contract. In Proceedings of the 2020 4th International Conference on Cryptography, Security and Privacy, Nanjing, China, 10–12 January 2020; pp. 89–94. [Google Scholar] [CrossRef][Green Version]
  71. Zichichi, M.; Ferretti, S.; Rodríguez-Doncel, V. Decentralized Personal Data Marketplaces: How Participation in a DAO Can Support the Production of Citizen-Generated Data. Sensors 2022, 22, 6260. [Google Scholar] [CrossRef] [PubMed]
  72. De Caldas Filho, F.L.; De Mendonça, F.L.; E Martins, L.M.; Da Costa, J.P.C.; Araújo, I.P.; De Sousa Júnior, R.T. Design and evaluation of a semantic gateway prototype for IoT networks. In Proceedings of the UCC 2017 Companion—Companion Proceedings of the 10th International Conference on Utility and Cloud Computing, Austin, TX, USA, 5–8 December 2017; pp. 195–201. [Google Scholar] [CrossRef]
  73. De Mulder, G.; De Meester, B.; Heyvaert, P.; Taelman, R.; Dimou, A.; Verborgh, R. PROV4ITDaTa: Transparent and direct transferof personal data to personal stores. Companion World Wide Web Conf. 2021, 1, 695–697. [Google Scholar] [CrossRef]
  74. Esteves, B.; Pandit, H.J.; Rodriguez-Doncel, V. ODRL Profile for Expressing Consent through Granular Access Control Policies in Solid. In Proceedings of the 2021 IEEE European Symposium on Security and Privacy Workshops, Euro S and PW 2021, Vienna, Austria, 6–10 September 2021; pp. 298–306. [Google Scholar] [CrossRef]
  75. Singh, B.C.; Carminati, B.; Ferrari, E. Learning Privacy Habits of PDS Owners. In Proceedings of the International Conference on Distributed Computing Systems, Atlanta, GA, USA, 5–8 June 2017; pp. 151–161. [Google Scholar] [CrossRef]
  76. Meurisch, C.; Werner, D.; Giger, F.; Bayrak, B.; Mühlhäuser, M. PDSproxy++: Proactive proxy deployment for confidential ad-hoc personalization of AI services. In Proceedings of the International Conference on Computer Communications and Networks, ICCCN, Honolulu, HI, USA, 3–6 August 2020. [Google Scholar] [CrossRef]
  77. HAT Project Research Team. HAT Briefing Paper 2: The Hub-of-All-Things (HAT) Economic Model of the Multisided Market Platform and Ecosystem; WMG Service Systems Research Group Working Paper Series (Number 02/15). 2015. Available online: (accessed on 9 November 2022).
  78. Mydex CIC. The Case for Personal Information Empowerment: The rise of the personal data store. World 2010, 1–44. Available online: (accessed on 9 November 2022).
  79. Papadopoulou, E.; Stobart, A.; Taylor, N.K.; Williams, M.H. Enabling data subjects to remain data owners. Proc. Smart Innov. Syst. Technol. 2015, 38, 239–248. [Google Scholar] [CrossRef]
  80. Mun, M.; Hao, S.; Mishra, N.; Shilton, K.; Burke, J.; Estrin, D.; Hansen, M.; Govindan, R. Personal data vaults: A locus of control for personal data streams. In Proceedings of the 6th International Conference on Emerging Networking Experiments and Technologies, Co-NEXT’10, Philadelphia, PA, USA, 30 November–3 December 2010. [Google Scholar] [CrossRef]
  81. Mun, M.Y.; Kim, D.H.; Shilton, K.; Estrin, D.; Hansen, M.; Govindan, R. PDVLoc: A personal data vault for controlled location data sharing. ACM Trans. Sens. Netw. 2014, 10, 1–29. [Google Scholar] [CrossRef]
  82. Shilton, K.; Burke, J.A.; Estrin, D.; Hansen, M. Designing the Personal Data Stream: Enabling Participatory Privacy in Mobile Personal Sensing. In Proceedings of the Research Conference on Communications, Information and Internet Policy, Washington, DC, USA, 16–17 September 2009; pp. 25–27. [Google Scholar]
  83. Jalali, L.; Jain, R. Building health persona from personal data streams. In Proceedings of the 1st ACM International Workshop on Personal Data Meets Distributed Multimedia, Co-located with ACM Multimedia 2013, Barcelona, Spain, 22 October 2013; pp. 19–26. [Google Scholar] [CrossRef]
  84. Available online: (accessed on 9 November 2022).
  85. Alén-Savikko, A.; Byström, N.; Hirvonsalo, H.; Honko, H.; Kallonen, A.; Kortesniemi, Y.; Kuikkaniemi, K.; Paaso, T.; Pitkänen, O.; Poikola, A.; et al. MyData Architecture—Consent Based Approach for Personal Data Management. 2016. Available online: (accessed on 9 November 2022).
  86. Mazeh, I.; Shmueli, E. A personal data store approach for recommender systems: Enhancing privacy without sacrificing accuracy. Expert Syst. Appl. 2020, 139, 112858. [Google Scholar] [CrossRef]
  87. Van Kleek, M.; Smith, D.; Shadbolt, N.; Schraefel, M. A decentralized architecture for consolidating personal information ecosystems: The WebBox. In Proceedings of the Pim 2012, Seattle, WA, USA, 11 February 2012; Available online: (accessed on 9 November 2022).
  88. Mansour, E.; Sambra, A.V.; Hawke, S.; Zereba, M.; Capadisli, S.; Ghanem, A.; Aboulnaga, A.; Berners-Lee, T. A Demonstration of the Solid Platform for Social Web Applications. In Proceedings of the 25th International Conference on World Wide Web, Montreal, QC, Canada, 11–15 April 2016; pp. 223–226. [Google Scholar] [CrossRef][Green Version]
  89. Available online: (accessed on 9 November 2022).
  90. Gabrielli, S.; Krenn, S.; Pellegrino, D.; Spaces, J.P.B. KRAKEN: A Secure, Trusted, Regulatory-Compliant, and Privacy-Preserving Data Sharing Platform. In Data Spaces: Design, Deployment and Future Directions; Springer: Berlin/Heidelberg, Germany, 2022; pp. 107–130. [Google Scholar]
  91. PIMCity—Building the Next Generation Personal Data Platforms. Available online: (accessed on 9 November 2022).
  92. FhG, L.; Heitmann, R. TRUSTS Trusted Secure Data Sharing Space D3. 9 Platform Status Report I; Technical Report 871481. 2021. Available online: (accessed on 9 November 2022).
  93. Daniela, B.Y.; Campos, Q.D.E. Searching Heterogeneous Personal Data. Ph.D. Thesis, School of Graduate Studies Rutgers, The State University of New Jersey, New Brunswick, NJ, USA, 2019. [Google Scholar]
  94. Choe, E.K.; Lee, N.B.; Lee, B.; Pratt, W.; Kientz, J.A. Understanding quantified-selfers’ practices in collecting and exploring personal data. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Toronto, ON, Canada, 26 April–1 May2014; pp. 1143–1152. [Google Scholar] [CrossRef]
  95. Chowdhury, M.J.M.; Colman, A.; Kabir, M.A.; Han, J.; Sarda, P. Blockchain as a Notarization Service for Data Sharing with Personal Data Store. In Proceedings of the 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018, New York, NY, USA, 1–3 August 2018; pp. 1330–1335. [Google Scholar] [CrossRef]
  96. Yan, Z.; Gan, G.; Riad, K. BC-PDS: Protecting Privacy and Self- Sovereignty through BlockChains for OpenPDS. In Proceedings of the IEEE Symposium on Service-Oriented System Engineering (SOSE), San Francisco, CA, USA, 6–9 April 2017; pp. 138–144. [Google Scholar]
  97. Otto, B.; Ten, M.; Wrobel, H.S. Designing Spaces Data The Ecosystem Approach to Competitive Advantage; Springer Nature Switzerland AG: Cham, Switzerland, 2022. [Google Scholar] [CrossRef]
  98. Curry, E.; Simon, S.; Tuikka, T. Data Spaces Design, Deployment and Future Directions; Springer Nature Switzerland AG: Cham, Switzerland, 2022; p. 357. [Google Scholar] [CrossRef]
  99. Curry, E.; Scerri, S.; Tuikka, T. Data Spaces: Design, Deployment, and Future Directions. In Data Spaces; Springer: Berlin/Heidelberg, Germany, 2022; pp. 1–17. [Google Scholar] [CrossRef]