Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
SpringerLink
Log in

SPIRIT: Semantic and Systemic Interoperability for Identity Resolution in Intelligence Analysis

  • Conference paper
  • First Online:
AI Approaches to the Complexity of Legal Systems XI-XII (AICOL 2020, AICOL 2018, XAILA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13048))

  • 808 Accesses

Abstract

This paper introduces the SPIRIT H2020 Project. The SPIRIT identity resolution service has been designed to learn about identity patterns, to build up a social graph related to them, and thereby facilitate LEA’s investigation work. The paper will briefly discuss the main task of identity resolution, the privacy controller system, the SPIRIT prototype that will realise the solution, and the ontology to embed privacy into the system. It also discusses a specific technical and legal challenge—i.e., semantic interoperability when integrating SPIRIT data—and its coordination at the agency level with human decision making—systemic interoperability. This paper takes into account the SPIRIT testing prototype and the first revision version (proof of concept prototype).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    SPIRIT. Scalable privacy preserving intelligence analysis for resolving identities. https://cordis.europa.eu/project/id/786993.

  2. 2.

    https://www.docker.com/.

  3. 3.

    https://syncope.apache.org/.

  4. 4.

    https://www.postgresql.org/.

  5. 5.

    https://www.arangodb.com/.

  6. 6.

    https://www.rabbitmq.com/.

  7. 7.

    https://graphql.org/.

  8. 8.

    http://valcri.org/.

  9. 9.

    https://www.w3.org/OWL/.

  10. 10.

    https://www.w3.org/RDF/.

  11. 11.

    https://dpvcg.github.io/dpv/.

  12. 12.

    https://github.com/dpvcg/dpv-gdpr.

  13. 13.

    ISA2 Programme (2016–2020) supported ‘the development of digital solutions that enable public administrations, businesses and citizens in Europe to benefit from interoperable cross-border and cross-sector public services.’ https://ec.europa.eu/isa2/isa2_en.

References

  1. Blomqvist, E., Davarakis, C.: Intelligence analysis and semantic interoperability for identity resolution (abstract). TWSDetection, Toulouse, 27–28 February 2020. http://ceur-ws.org/Vol-2606/12invited.pdf

  2. Casanovas, P., Morris, N., González-Conejero, J., Teodoro, E., Adderley, R.: Minimisation of incidental findings, and residual risks for security compliance: the SPIRIT Project. In: TERECOM@JURIX 2018, pp. 85–96. CEUR 2309 (2018). https://ceur-ws.org/Vol-2309/09.pdf

  3. Adderley, R., Adderley, S., Rovatsou, R., Kazemian, H., Raffaelli, M., Ferrara, F.: Blomqvist: SPIRIT. Deliverable n° D6.2. Resource Flow Pattern Recognition Algorithms (WP6), 30 November 2019

    Google Scholar 

  4. Casanovas, P., Morris, N., Teodoro, E., González-Conejero, J., Adderley, R.: SPIRIT. Deliverable n. D9.2. Incidental Findings Policy (WP9), 31 October 2018. https://doi.org/10.5281/zenodo.3815050

  5. Hartig, O., Blomqvist, E., Capshaw, R., Raffaelli, M., Adderley, R., Kazemian, H.: SPIRIT. Deliverable n. ° D5.1. Graph Infrastructure and Analysis (Report and software) (WP5), 30 November 2019

    Google Scholar 

  6. Botoeva, E., Calvanese, D., Cogrel, B., Corman, J., Xiao, G.: Ontology-based data access – beyond relational sources. Intelligenza Artificiale 13(1), 21–36 (2019). IOS Press

    Google Scholar 

  7. Tiemann, M., Badii, L., Faulkner, R.: SPIRIT. Deliverable n. 9.7 Privacy Controller for Modelling and Filtering Software and Report (a). (WP2), 30 October 2019

    Google Scholar 

  8. Bartolini, C., Muthuri, R.: Reconciling data protection rights and obligations: an ontology of the forthcoming EU regulation. In: Workshop on Language and Semantic Technology for Legal Domain, p. 8 (2015). https://orbilu.uni.lu/bitstream/10993/21969/1/main.pdf

  9. Palmirani, M., Martoni, M., Rossi, A., Bartolini, C., Robaldo, L.: PrOnto: privacy ontology for legal reasoning. In: Kő, A., Francesconi, E. (eds.) EGOVIS 2018. LNCS, vol. 11032, pp. 139–152. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98349-3_11

    Chapter  Google Scholar 

  10. Pandit, H.J., Lewis, D.: Modelling provenance for GDPR compliance using linked open data vocabularies. In Proceedings of the 5th Workshop on Society, Privacy and the Semantic Web - Policy and Technology (PrivOn2017) (PrivOn) (2017). http://ceur-ws.org/Vol-1951/PrivOn2017_paper_6.pdf

  11. Jonas, J.: Identity resolution: 23 years of practical experience and observations at scale. In: 2006 ACM SIGMOD International Conference on Management of Data, p. 718 (2006)

    Google Scholar 

  12. Bartunov, S., Korshunov, A., Park, S.T., Ryu, W., Lee, H.: Joint link-attribute user identity resolution in online social networks. In: The 6th SNA-KDD Workshop 2012 (SNA-KDD 2012), 12 August. ACM (2012)

    Google Scholar 

  13. Fu, X., Boongoen, T., Shen, Q.: Evidence directed generation of plausible crime scenarios with identity resolution. Appl. Artif. Intell. 24(4), pp. 253–276 (2010)

    Google Scholar 

  14. Papageorgiou, A., Strigkos, M., Politou, E., Alepis, E., Solanas, A., Patsakis, C.: Security and privacy analysis of mobile health applications: the alarming state of practice. IEEE Access 6, 9390–9403 (2018)

    Article  Google Scholar 

  15. Zimmeck, S., et al.: Maps: scaling privacy compliance analysis to a million apps. Proc. Priv. Enhanc. Technol. 3, 66–86 (2019)

    Google Scholar 

  16. Torra, V.: Data privacy: Foundations, New Developments and the Big Data Challenge. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57358-8

  17. EU 2016: Directive (EU) 2016/680 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data by competent authorities for the purposes of the prevention, investigation, detection or prosecution of criminal offences or the execution of criminal penalties, and on the free movement of such data, and repealing Council Framework Decision 2008/977/JHA. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32016L0680

  18. EU 2016: Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). https://eur-lex.europa.eu/eli/reg/2016/679/oj

  19. Casanovas, P., Mendelson, D., Poblet, M.: A linked democracy approach for regulating public health data. Heal. Technol. 7(4), 519–537 (2017)

    Article  Google Scholar 

  20. EU 2017: New European interoperability framework promoting seamless services and data flows for European public administrations. Publications Office of the European Union, Luxembourg (2017). https://doi.org/10.2799/78681

  21. EU 2007: DIRECTIVE 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community (INSPIRE). https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32007L0002&from=EN

  22. EU 2017: Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of The Regions. European Interoperability Framework – Implementation Strategy. Brussels, 23.3.2017 COM(2017) 134 final. https://eur-lex.europa.eu/resource.html?uri=cellar:2c2f2554-0faf-11e7-8a35-01aa75ed71a1.0017.02/DOC_1&format=PDF

  23. EU 2005: Communication from the Commission to the Council and the European Parliament on improved effectiveness, enhanced interoperability and synergies among European databases in the area of Justice and Home Affairs, Brussels, 24.11.2005 COM(2005) 597 final

    Google Scholar 

  24. De Hert, P., Gutwirth, S.: Interoperability of police databases within the EU: an accountable political choice? Int. Rev. Law Comput. Technol. 20(1–2), 21–35 (2006)

    Google Scholar 

  25. EU 2017: Proposal for a Regulation of the European Parliament and of the Council on establishing a framework for interoperability between EU information systems (police and judicial cooperation, asylum and migration). Brussels, 12.12.2017 COM(2017) 794 final 2017/0352 (COD)

    Google Scholar 

  26. Quintel, T.: Interoperability of EU Databases and Access to Personal Data by National Police Authorities under Article 20 of the Commission Proposals. Eur. Data Prot. L. Rev. 4, 470–482 (2018)

    Google Scholar 

  27. Kubicek, H., Cimander, R., Scholl, H.J.: Layers of Interoperability. In: Kubicek, H., Cimander, R., Scholl, H.J. (eds.) Organizational Interoperability in E-Government, pp. 85–96. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22502-4_7

  28. Dignum, V.: Responsible Artificial Intelligence: How to Develop and use AI in a Responsible Way. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30371-6

  29. Pagallo, U., Casanovas, P., Madelin, R., Dignum, V., et al.: AI4People. 2019. On Good AI Governance. 14 Priority Actions as SMART Model of Governance, and a Regulatory Toolbox (2019). https://www.eismd.eu/wp-content/uploads/2019/11/AI4Peoples-Report-on-Good-AI-Governance_compressed.pdf

  30. Whittlestone, J., Nyrup, R., Alexandrova, A.,Dihal, K., Cave, S.: Ethical and Societal Implications of Algorithms, Data, and Artificial Intelligence: A Roadmap for Research. Nuffield Foundation, London (2019)

    Google Scholar 

  31. Bonatti, P.A., Kirrane, S., Petrova, I.M., Sauro, L.: Machine Understandable Policies and GDPR Compliance Checking. arXiv preprint arXiv:2001.08930 (2020)

  32. Governatori, G., Hashmi, M., Lam, H.-P., Villata, S., Palmirani, M.: Semantic business process regulatory compliance checking using LegalRuleML. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds.) EKAW 2016. LNCS (LNAI), vol. 10024, pp. 746–761. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49004-5_48

    Chapter  Google Scholar 

Download references

Acknowledgments

SPIRIT. Scalable Privacy Preserving Intelligence Analysis for Resolving Identities. European Commission. Contract 786993. 01/08/2018-31/07/2021.

Author information

Authors and Affiliations

  1. Singular Logic, SPIRIT, Athens, Greece

    Costas Davarakis

  2. Department of Computer and Information Science, Linköping University, Linköping, Sweden

    Eva Blomqvist

  3. Innova Integra Ltd., Reading, United Kingdom

    Marco Tiemann

  4. La Trobe Law School, La Trobe University, Melbourne, Australia

    Pompeu Casanovas

  5. Institute of Law and Technology, Autonomous University of Barcelona, Barcelona, Spain

    Pompeu Casanovas

Authors
  1. Costas Davarakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eva Blomqvist
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marco Tiemann
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pompeu Casanovas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Costas Davarakis .

Editor information

Editors and Affiliations

  1. Technical University of Madrid, Madrid, Spain

    Víctor Rodríguez-Doncel

  2. University of Bologna, Bologna, Italy

    Monica Palmirani

  3. Jagiellonian University, Krakow, Poland

    Michał Araszkiewicz

  4. La Trobe University, Melbourne, VIC, Australia

    Pompeu Casanovas

  5. University of Turin, Turin, Italy

    Ugo Pagallo

  6. University of Bologna, Bologna, Italy

    Giovanni Sartor

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Davarakis, C., Blomqvist, E., Tiemann, M., Casanovas, P. (2021). SPIRIT: Semantic and Systemic Interoperability for Identity Resolution in Intelligence Analysis. In: Rodríguez-Doncel, V., Palmirani, M., Araszkiewicz, M., Casanovas, P., Pagallo, U., Sartor, G. (eds) AI Approaches to the Complexity of Legal Systems XI-XII. AICOL AICOL XAILA 2020 2018 2020. Lecture Notes in Computer Science(), vol 13048. Springer, Cham. https://doi.org/10.1007/978-3-030-89811-3_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-89811-3_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89810-6

  • Online ISBN: 978-3-030-89811-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation