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Regulation of Big Data: Perspectives on strategy, policy, law and privacy

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Abstract

This article encapsulates selected themes from the Australian Data to Decisions Cooperative Research Centre’s Law and Policy program. It is the result of a discussion on the regulation of Big Data, especially focusing on privacy and data protection strategies. It presents four complementary perspectives stemming from governance, law, ethics, and computer science. Big, Linked, and Open Data constitute complex phenomena whose economic and political dimensions require a plurality of instruments to enhance and protect citizens’ rights. Some conclusions are offered in the end to foster a more general discussion.

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Notes

  1. The article reflects papers delivered at “Regulation of Big Data”, a panel discussion held at Deakin University, Australia, August 3rd 2016. While the support of the Data to Decisions Cooperative Research Centre is acknowledged, the views expressed in this article do not necessarily reflect the views of the Centre or of other members of the Law and Policy Program.

  2. Douglas Laney is a VP and Distinguished Analyst with Gartner’s Chief Data Officer Research team.

  3. [1]: “There are many definitions of “Big Data” which may differ depending on whether you are a computer scientist, a financial analyst, or an entrepreneur pitching an idea to a venture capitalist. Most definitions reflect the growing technological ability to capture, aggregate, and process an ever-greater volume, velocity, and variety of data.”

  4. School of Business and Management, Lappeenranta University of Technology, Finland.

  5. See for example [2]: “Big Data refers to both large volumes of data with high level of complexity and the analytical methods applied to them which require more advanced techniques and technologies in order to derive meaningful information and insights in real time”.

  6. The Canadian component was led by Dr. Alana Maurushat of UNSW Law.

  7. The division of participants among the three countries is as follows: 38 participants were from Australia (interviewed from 25 March 2015 to 13 November 2015), 14 were from the UK (interviewed from 24 February 2016 and 18 March 2016) and 11 were from Canada (interviewed from 15 October 2015 to 26 February 2016). For the methodology employed, see [13].

  8. Discussions in sub-sections 1.3, 1.4 and 1.5 are largely based on [14].

  9. See the discussion between J. Cannataci, C. Nyst, F. Patel and L. McGregor at Geneva Academy [17], and G. Greenleaf’s comments on the Report [18].

  10. In 16 c. England, the visitations commenced in 1535, the inventory powers were granted by Parliament in 1536, and the process might have carried on for a few years. See for a cultural analysis of the ambivalent political roles that lists and cards can play, Werbin [65]. The author traces the history of Big Data “back to the earliest forms of punch cards, sorters and tabulators emerging in the late nineteenth century when these technologies of population control were first developed by Herman Hollerith (founder of IBM) while working at the US Census Bureau “.

  11. Open Knowledge International is a global non-profit organisation “focused on realising open data’s value to society by helping civil society groups access and use data to take action on social problems”. Cf. https://okfn.org/about/

  12. Former Information and Privacy Commissioner for the Canadian province of Ontario serving from 1997 to 2014. She is currently the Executive Director of the Privacy and Big Data Institute at Ryerson University.

  13. Senior Analyst, Information Technology and Innovation Foundation.

  14. See the seminal and influential white paper published in 2008 by the still unidentified author (or authors) under the pseudonym of ‘Satoshi Nakamoto‘ [25] .

  15. Including Regulation of Investigatory Powers Act 2000 (UK), Police Act 1997 (UK), Justice and Security Act 2013 (UK), Counter-Terrorism and Security Act 2015 (UK), and Data Retention and Investigatory Powers Act 2014 (UK).

  16. The United Kingdom, Australia, and Canada belong to the common law family of legal systems; are constitutional monarchies; they are bound by the multilateral United Kingdom – United States of America Agreement for cooperation in signals intelligence, known as Five Eyes.

  17. Provisions of the Investigatory Powers Act include not only statutory controls on the issuance and approval of warrants, but also framework for oversight of the access to and gathering of communications and bulk sets of data, their use and management (distribution, retention and destruction).

  18. Investigatory Powers Act 2016 (UK) s 2(1).

  19. To access data from computers, smartphones etc. by the security and intelligence agencies, law enforcement and the armed forces.

  20. The new Draft Code of Practice on Equipment Interference for the security and intelligence agencies identifies the following objectives:

    “a) obtain information from the equipment in pursuit of intelligence requirements;

    b) obtain information concerning the ownership, nature and use of the equipment with a view to meeting intelligence requirements;

    c) locate and examine, remove, modify or substitute equipment hardware or software which is capable of yielding information of the type described in (a) and (b);

    d) enable and facilitate surveillance activity by means of the equipment;

    “Information” may include communications content, and communications data.”

  21. Investigatory Powers Act 2016 (UK) s 141.

  22. Investigatory Powers Act 2016 (UK) s160.

  23. Investigatory Powers Act 2016 (UK) s182.

  24. Investigatory Powers Act 2016 (UK) s 211.

  25. Investigatory Powers Act 2016 (UK), s 18 and s 20.

  26. Investigatory Powers Act 2016 (UK), s 19.

  27. Investigatory Powers Act s 30 Renewal (s 33(9)(b)), notification and major modifications (s 37(3) and s 38) must be personally approved by the Secretary of State, or in the case of a warrant to be issued by the Scottish Ministers, a member of the Scottish Government. Decision to issue warrants to intelligence services are to be taken personally by the Secretary of State or, where relevant, by a member of the Scottish Government Ministers (105); as are decisions involving renewals of warrants (ss 117), major modifications (s 120, s 122).

  28. Investigatory Powers Act 2016, s 18: “(1) Each of the following is an “intercepting authority” for the purposes of this Part—

    (a) a person who is the head of an intelligence service; (b) the Director General of the National Crime Agency; (c) the Commissioner of Police of the Metropolis; (d) the Chief Constable of the Police Service of Northern Ireland; (e) the chief constable of the Police Service of Scotland [separate warrantry regime]; (f) the Commissioners for Her Majesty’s Revenue and Customs; (g) the Chief of Defence Intelligence;

    (h) a person who is the competent authority of a country or territory outside the United Kingdom for the purposes of an EU mutual assistance instrument or an international mutual assistance agreement.”

  29. Warrants made under the relevant mutual legal assistance treaty to which the United Kingdom is party for the purpose of gathering and exchanging information/data.

  30. Investigatory Powers Act 2016 (UK) s 20(2)(c) provides that a “targeted interception warrant or targeted examination warrant is necessary “in the interests of the economic well-being of the United Kingdom so far as those interests are also relevant to the interests of national security”, but only “if the information which it is considered necessary to obtain is information relating to the acts or intentions of persons outside the British Islands” (s 20 (4).

  31. Appointments of Judicial Commissioners will be made by the Prime Minister after consultation with the Lord Chief Justice of England and Wales, the Lord President of Scotland, the Lord Chief Justice of Northern Ireland, the Scottish Ministers, and the First Minister and deputy First Minister in Northern Ireland.

  32. Judicial Commissioners have the power to approve both, warrants issued by the Secretary of State and those issued by Scottish Ministers under s 23 of the Investigatory Powers Act 2016. (1) In deciding whether to approve a person’s decision to issue a warrant under this Chapter, a Judicial Commissioner must review the person’s conclusions as to the following matters— (a) whether the warrant is necessary on relevant grounds (see subsection (3)), and (b) whether the conduct that would be authorised by the warrant is proportionate to what is sought to be achieved by that conduct. (2) In doing so, the Judicial Commissioner must— (a) apply the same principles as would be applied by a court on an application for judicial review, and (b) consider the matters referred to in subsection (1) with a sufficient degree of care as to ensure that the Judicial Commissioner complies with the duties imposed by section 2 (general duties in relation to privacy). (3) In subsection (1)(a) “relevant grounds” means— (a) in the case of a decision of the Secretary of State to issue a warrant, grounds falling within section 20; (b) in the case of a decision of the Scottish Ministers to issue a warrant, grounds falling within section 21(4).” The Advocate-General for Scotland (Lord Keen of Elie) [31].

  33. There are two procedural control mechanisms: Section 23(4) of the Investigatory Powers Act 2016 requires the Judicial Commissioner who refuses to approve a person’s decision to issue a warrant to provide written reasons for the refusal; and s 23(5) provides that where “a Judicial Commissioner, other than the Investigatory Powers Commissioner, refuses to approve a person’s decision to issue a warrant …, the person may ask the Investigatory Powers Commissioner to decide whether to approve the decision to issue the warrant”.

  34. The common law test of proportionality differs from the EU formulation of proportionality. Lumsdon & Ors, R v Legal Services Board [2015] UKSC 41; [2015] 3 WLR 121; Bank Mellat v HM Treasury (No 2) [2013] UKSC 39, [2013] 3 WLR 179.

  35. Under s 227 of the Investigatory Powers Act 2016, the complex process of appointment is as follows: “Investigatory Powers Commissioner and other Judicial Commissioners (1) The Prime Minister must appoint— (a) the Investigatory Powers Commissioner, and (b) such number of other Judicial Commissioners as the Prime Minister considers necessary for the carrying out of the functions of the Judicial Commissioners. (2) A person is not to be appointed as the Investigatory Powers Commissioner or another Judicial Commissioner unless the person holds or has held a high judicial office (within the meaning of Part 3 of the Constitutional Reform Act 2005). (3) A person is not to be appointed as the Investigatory Powers Commissioner unless recommended jointly by— (a) the Lord Chancellor, (b) the Lord Chief Justice of England and Wales, (c) the Lord President of the Court of Session, and (d) the Lord Chief Justice of Northern Ireland. (4) A person is not to be appointed as a Judicial Commissioner under subsection (1)(b) unless recommended jointly by— (a) the Lord Chancellor, (b) the Lord Chief Justice of England and Wales, (c) the Lord President of the Court of Session, (d) the Lord Chief Justice of Northern Ireland, and (e) the Investigatory Powers Commissioner. (5) Before appointing any person under subsection (1), the Prime Minister must consult the Scottish Ministers. (6) The Prime Minister must have regard to a memorandum of understanding agreed between the Prime Minister and the Scottish Ministers when exercising functions under subsection (1) or (5). (7) The Investigatory Powers Commissioner is a Judicial Commissioner and the Investigatory Powers Commissioner and the other Judicial Commissioners are to be known, collectively, as the Judicial Commissioners”.

  36. In the Investigatory Powers Act 2016 (UK), s 1, Parts 2 to 7 and Part 8; as well as by virtue of the Human Rights Act 1998 (UK); Data Protection Act 1998 (UK), s 55 (unlawful obtaining etc. of personal data); Wireless Telegraphy Act 2006 (UK), s 48 (offence of interception or disclosure of messages); Computer Misuse Act 1990 (UK), s 1 to 3A (computer misuse offences).

  37. This discussion is based on Casanovas [32] and Casanovas et al. [33].

  38. http://www.gartner.com/technology/research/hype-cycles/

  39. See GHENT 2015, ibid. “A data scientist can be defined as a person who creates or generates models that leverage predictive or prescriptive analytics but whose primary job function is outside of the field of statistics and analytics.”

  40. http://www.gartner.com/technology/research/hype-cycles/

  41. Vice president at the Information Technology and Innovation Foundation (ITIF) and director of ITIF’s Center for Data Innovation.

  42. Research analyst with The Information Technology and Innovation Foundation (ITIF).

  43. This discussion is based on Casanovas et al. [33].

  44. http://wiki.dbpedia.org/Datasets, http://wiki.dbpedia.org/

  45. http://wiki.dbpedia.org/about

  46. https://www.wikidata.org/wiki/Wikidata:Main_Page

  47. https://learn.canvas.net/courses/4/pages/creative-commons-licenses

  48. http://ontologydesignpatterns.org/wiki/Main_Page

  49. These semantic tools are constructed within a cooperative and collective work of knowledge engineering (with end-users’ cooperation). Semantics, constructing ontologies and ODP, means eliciting and sharing knowledge, making it explicit. See some examples [45] [46].

  50. See the work by Renato Iannella et al. at https://www.w3.org/community/odrl/

  51. Differential privacy aims to provide means to maximize the accuracy of queries from statistical databases while minimizing the chances of identifying its records.

    Cfr. https://en.wikipedia.org/wiki/Differential_privacy

  52. See Dwork [47]: “Differential privacy is a strong privacy guarantee for an individual’s input to a (randomized) function or sequence of functions, which we call a privacy mechanism. Informally, the guarantee says that the behaviour of the mechanism is essentially unchanged independent of whether any individual opts into or opts out of the data set. Designed for statistical analysis, for example, of health or census data, the definition protects the privacy of individuals, and small groups of individuals, while permitting very different outcomes in the case of very different data sets”.

  53. As stated by Dwork and Roth [49]: “Differential privacy describes a promise, made by a data holder, or curator, to a data subject: ‘You will not be affected, adversely or otherwise, by allowing your data to be used in any study or analysis, no matter what other studies, data sets, or information sources, are available.’ At their best, differentially private database mechanisms can make confidential data widely available for accurate data analysis, without resorting to data clean rooms, data usage agreements, data protection plans, or restricted views. Nonetheless, data utility will eventually be consumed: the Fundamental Law of Information Recovery states that overly accurate answers to too many questions will destroy privacy in a spectacular way. The goal of algorithmic research on differential privacy is to postpone this inevitability as long as possible.”

  54. On 8 April 2016 the Council adopted the Regulation and the Directive. On 14 April so did the European Parliament. On 4 May, the official texts were published in the EU Official Journal (in all the official languages). The Regulation entered into force on 24 May, and it shall apply from 25 May 2018. The Directive, on 5 May 2016, and the EU states should transpose it into their national laws before 6 May 2018.

  55. https://ec.europa.eu/priorities/digital-single-market_en

  56. This American judicial tradition of property under some constraints to protect individual rights has been interpreted by Morton Horwitz [56] as a benefit for the economic development of entrepeneurs and companies, creating the legal conditions for 20 c. liberal capitalism.

  57. Two different Western cultures: “On the one hand, a European interest in personal dignity, threatened primarily by the mass media; on the other hand, an American interest in liberty, threatened primarily by the government” [60].

References

  1. Executive Office of the US President. Big Data: Seizing Opportunities, Preserving Values (White House). Published May 1. Council on Foreign Relations (mar 2017). http://wwwcfrorg/technology-and-science/white-house-big-data---seizing-opportunities-preserving-values/p32916 Cited. October 5 2016.

  2. UK Government. Emerging Technologies Big Data Community of Interest, ‘Emerging Technologies: Big Data: A Horizon Scanning Research Paper’ (HM Government Horizon Scanning Programme. Cabinet Office. First published: 18 November 2014. Available at: https://www.gov.uk/government/publications/emerging-technologies-big-data. Cited October 5 2016.

  3. Australian Government Information Management Office. ‘Australian Public Service Big Data Strategy - Improved Understanding Through Enhanced Data-Analytics Capability’. 2013. Department of finance and deregulation. ISBN: 978–1–922096-27-2 Available at: http://wwwfinancegovau/sites/default/files/the-australian-public-service-big-data-strategy-archivedpdf Cited 5 October 2016.

  4. Australian Government. ‘Australian Public Service Better Practice Guide for Big Data’. 2015. Version 2.0. January 2015. Joint work of the Data Analytics Centre of Excellence (chaired by the Australian Taxation Office) and the Big Data Working Group (chaired by the Department of Finance). ISBN: 978–1–922096-31-9 Available at: http://www.finance.gov.au/sites/default/files/APS-Better-Practice-Guide-for-Big-Data.pdf. Cited 5 October 2016.

  5. Diebold FA. Personal Perspective on the Origin(s) and Development of 'Big Data': The Phenomenon, the Term, and the Discipline, PIER Working Paper No 13–003, 2nd Version. 2012. Available at: http://ssrn.com/abstract=2202843. Cited 5 October 2015.

  6. Cox M, Ellsworth D. Application-Controlled Demand Paging for Out-of-Core Visualization. Proceedings of the 8th Conference on Visualization, IEEE Computer Society Press; 1997.

  7. Laney D. 3D data management: controlling data volume, velocity and variety. META Group Research Note No. 2001;6:2001.

    Google Scholar 

  8. Ylijoki O, Porras J. Perspectives to definition of Big Data: A mapping study and discussion. Journal of Innovation Management. 2016;4(1):69–91.

    Google Scholar 

  9. Bennett Moses L. Bridging distances in approach: Sharing ideas about technology regulation. In R. Leenes, E. Kosta (editors) Bridging distances in technology and regulation, Oisterwijk: Wolf Legal Pub.; 2013. 37–51.

  10. Bijker WE. Of Bicycles, Bakelites, and Bulbs: Towards a Theory of Sociotechnical Change, MIT Press; 1997.

  11. Orlikowski WJ, Gash DC. Technological frames: Making sense of information technology in organisations. ACM Trans Inf Syst. 1994;12(2):174–207.

    Article  Google Scholar 

  12. Chan J, et al. The technological game: How information technology is transforming police practice. Criminal Justice. 2001;1(2):139–59.

    Article  Google Scholar 

  13. Law and Policy Program, Data to Decisions CRC. Big Data Technology and National Security - Comparative International Perspectives on Strategy, Policy and Law: Methodology (Data to Decisions CRC). Adelaide. 2016.

  14. Law and Policy Program, Data to Decisions CRC. Big Data Technology and National Security- Comparative International Perspectives on Strategy, Policy and Law: Comparative Study (Data to Decisions CRC, 2016). Adelaide 2016.

  15. Law and Policy Program, Data to Decisions CRC. Big Data Technology and National Security - Comparative International Perspectives on Strategy, Policy and Law: Australia (Data to Decisions CRC). Adelaide. 2016.

  16. Cannataci JA. Report of the Special Rapporteur on the Right to Privacy. 2016. Available at: http://www.ohchr.org/EN/Issues/Privacy/SR/Pages/SRPrivacyIndex.asp. Cited 5 October 2016.

  17. Geneva Academy of International Humanitarian Law and Human Rights, Report of the Side event: The Right to Privacy in the Digital Age: Challenges and Ways Forward. 8 March 2016. Available at: http://www.geneva-academy.ch/docs/events/2016/GenevaAcademy%20FINAL%20REPORTsideevent%20privacy_8March2016-1.pdf. Cited 5 October 2016.

  18. Greenleaf G. UN Privacy Rapporteur Sets High Goals, 140 Privacy Laws & Business International, Report 10–12, 30 April 2016. UNSW Law Research Paper No. 2016–5. Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2801981. Cited 5 October 2016.

  19. NIST Big Data Public Working Group Definitions and Taxonomies Subgroup. NIST Special Publication 1500–1, NIST Big Data Interoperability Framework: Volume 1, Definitions. Final Version 1. 2015. Available at: doi:10.6028/NIST.SP.1500-1. Cited 5 October 2016.

  20. OpenKnowledge International. Open Data Handbook. What is Open Data?. 2016. Available at: http://opendatahandbook.org/guide/en/what-is-open-data/. Cited 5 October 2016.

  21. Australian Digital Transformation Office. Open data Improving services through accessible machine-readable data. Updated 21 July 2015. Available at: https://www.dto.gov.au/standard/design-guides/open-data/. Cited 5 October 2016.

  22. Australian Department of Health. Linkable de-identified 10% sample of Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Schedule (PBS). 2016. Available at: https://data.gov.au/dataset/mbs-sample-10pct-1984-gz Data temporally unavailable, 5 October 2016.

  23. Cavoukian A, Castro D. Big Data and Innovation, Setting the Record Straight: De-identification Does Work. 2014. Available at: http://www2.itif.org/2014-big-data-deidentification.pdf. Cited 5 October 2016.

  24. Narayanan A, Felten EW. (Princeton). No silver bullet: De-identification still doesn't work. 2014. Available at: http://randomwalker.info/publications/no-silver-bullet-de-identification.pdf. Cited 5 October 2016.

  25. Nakamoto S. [pseudonym]. Bitcoin: A peer-to-peer electronic cash system, White paper. Published for the first time in October 31st. 2008. Available at: http://satoshi.nakamotoinstitute.org/quotes/bitcoin-design/ Also available at: https://bitcoin.org/bitcoin.pdf. Cited 10 March 2017.

  26. Pentland Alex (MIT). Big Data’s Biggest Obstacles. 2012. Available at: https://hbr.org/2012/10/big-datas-biggest-obstacles. Cited 5 October 2016.

  27. Martin P, Hunter F. Minister says Census 'no worse than Facebook' as Nick Xenophon risks jail. The Sydney Morning Herald. 2016. Available at: http://www.smh.com.au/federal-politics/political-news/minister-says-census-no-worse-than-facebook-as-nick-xenophon-risks-jail-20160808-gqnobg.html. Cited 5 October 2016.

  28. Dent J. Ex-head of ASIO, David Irvine, on Data Retention Laws. 2015. The ethics Centre. Available at: http://wwwethicsorgau/on-ethics/blog/march/exclusive-ex-head-of-asio,-david-irvine,-on-data-r Cited 5 October 2016.

  29. Dingle S. Attorney-General George Brandis struggles to explain Government's metadata proposal. ABC News. Available at: http://www.abc.net.au/news/2014-08-07/brandis-explanation-adds-confusion-to-metadata-proposal/5654186. Cited 5 October 2016.

  30. Australian Government. Review Panel. Review of The Personally Controlled Electronic Health Record. Available at: https://health.gov.au/internet/main/publishing.nsf/Content/17BF043A41D470A9CA257E13000C9322/$File/FINAL-Review-of-PCEHR-December-2013.pdf. Cited 5 October 2016.

  31. The Advocate-General for Scotland (Lord Keen of Elie) (Con), “Investigatory Powers Bill” House of Lords Hansard, 13 July 2016, Volume 774 Column 228. Availlable at: https://hansardparliamentuk/lords/2016-07-13/debates/16071337000437/InvestigatoryPowersBill Cited 5 October 2016.

  32. Casanovas P. Conceptualisation of Rights and Meta-rule of Law for the Web of Data, Democracia Digital e Governo Eletrônico (Santa Caterina, Brazil) vol.12. 2015: 18-41; repr. Journal of Governance and Regulation / Volume 4, Issue 4, 2015; p. 118–129. Available at: http://buscalegis.ufsc.br/revistas/index.php/observatoriodoegov/article/viewFile/34399/33229. Cited 5 October 2016.

  33. Casanovas P, Rodríguez-Doncel V, González-Conejero J. The Role of Pragmatics in the Web of Data; in F. Poggi, A. Capone (Eds.) Pragmatics and Law. Practical and Theoretical Approaches, Berlin: Springer Verlag. 2016. pp. 293–330. DOI: 10.1007/978-3-319-44601-1_12. Available at SSRN: http://ssrn.com/abstract=2697832. Cited 5 October 2016.

  34. Rajaraman V. Big Data Analytics. Resonance. 2016;21:695. doi:10.1007/s12045-016-0376-7.

    Article  Google Scholar 

  35. Poblet M. (ed.). Mobile Technologies for Conflict Management. Online dispute resolution, governance, participation. LGTS n. 2, 2011, Dordrecht: Springer Verlag

  36. Corea, F. Big Data Analytics: A Management Perspective, Studies in Big Data 21, 2016, Switzerland, Springer Nature

  37. Williams S. Business intelligence strategy and Big Data analytics: a general management perspective. Amsterdam: Morgan Kaufmann, Elsevier; 2016.

  38. Castro, D., McQuinn, The Privacy Panic Cycle: A Guide to Public Fears about New Technologies, US Information Technology and Innovation Foundation. 2015. Available at: http://www2.itif.org/2015-privacy-panic.pdf. Cited 5 October 2016.

  39. Casanovas P. The Future of Law: Relational Law and Next Generation of Web Services, in M. Fernández-Barrera, P. de Filippi et al. (Eds.) The Future of Law and Technology: Looking into the Future. Selected Essays. European Press Academic Publishing, Legal Information and Communication Technologies Series, vol. 7, Florence. 2010. pp. 137–156. Available at: http://www.ejls.eu/6/205UK.htm. Cited 5 October 2016.

  40. Casanovas P. Open Source Intelligence, Open Social Intelligence and Privacy by Design. In ECSI-2014, CEUR 183, pp. 174–185, Available at: http://ceur-ws.org/Vol-1283/. Cited 5 October 2016.

  41. Barrett MA, Humblet O, Hiatt RA, Adler NE. Big Data and disease prevention: From qualified self to quantified communities. Big Data. 2013;1(3):168–75. doi:10.1089/big.2013.0027. http://online.liebertpub.com/doi/abs/10.1089/big.2013.0027. Cited 5 October 2016

  42. Schmachtenberg M, Bizer C, Jentzsch A, Cyganiak R. The Linking Open Data Cloud Diagram. 2014. Available at: http://Lod-Cloud.Net. Cited: 5 October 2016. See as a published paper: Schmachtenberg, M. et al. Adoption of the Linked Data Best practices in different topical domains, 13th International Semantic Web Conference, LCNS 8796, pp. 245-260. Springer International Publishing.

  43. Rodriguez-Doncel V, Gómez-Pérez A, Mihindukulasooriya N, Rights declaration in Linked Data. In: O. Hartig et al. (ed.) Proceedings of the Fourth International Workshop on Consuming Linked Data (COLD2013), CEUR 1034. 2014. Available at: http://ceur-ws.org/Vol-1034/RodriguezDoncelEtAl_COLD2013.pdf. Cited 5 October 2016.

  44. Paschke, A. Pragmatic Web 4.0. Towards an Active and Interactive Semantic Media Web, W3C Aspect of Semantic Technologies. 2013. Available at: http://www.slideshare.net/swadpasc/pragmatic-web-paschke. Cited 5 October 2015.

  45. Casellas N, Blázquez M, Kiryakov A, Casanovas P, Poblet M, Benjamins VR. OPJK into PROTON: Legal domain ontology integration into an upper-level ontology. In OTM Confederated International Conferences, On the Move to Meaningful Internet Systems, Springer Berlin, Heidelberg. 2005, pp. 846–855.

  46. Casanovas P, Casellas N, Tempich C., Vrandecic D., Benjamins VR. OPJK and DILIGENT: ontology modeling in a distributed environment. Artificial Intelligence and Law, 2007; 15 (2): 171–186.

  47. Dwork C. The differential privacy frontier. In: Theory of Cryptography Conference, Springer, LNCS 5444. Berlin: Springer; 2009. pp. 496–502.

  48. Dwork C. Differential privacy: a survey of results. In: In international Conference on theory and applications of models of computation, LNCS, Springer, Berlin, 4978; 2008.,pp. 1–19.

    Google Scholar 

  49. Dwork C, Roth A. The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science. 2014;9(3–4):211–407.

    MathSciNet  MATH  Google Scholar 

  50. Simonite T. Apple Rolls Out Privacy-Sensitive Artificial Intelligence, MIT Technology Review. 2016. Available at: https://www.technologyreview.com/s/601688/apple-rolls-out-privacy-sensitive-artificial-intelligence/. Cited 5 October 2016.

  51. Simonite T. Apple’s New Privacy Technology May Pressure Competitors to Better Protect Our Data, MIT Technology Review. 2016. Available at: https://www.technologyreview.com/s/602046/apples-new-privacy-technology-may-pressure-competitors-to-better-protect-our-data/?utm_campaign=content-distribution&utm_source=dlvr.it&utm_medium=twitter. Cited 5 October 2016.

  52. Hijmans, H. 2016. The European Union as Guardian of internet privacy: the story of art 16 TFEU, LGTS vol. 31, Dordrecht: Springer.

  53. Gutwirth S, Leenes R, De Hert P (Eds.). Data Protection on the Move. Current Developments in ICT and Privacy/Data Protection, Springer Verlag, Dordrecht, 2016.

  54. González-Fuster G. The emergence of personal data protection as a fundamental right of the EU. Springer, Dordrecht, LGT. 16: 2014

  55. De Hert P, Papakonstantinou V. The new General Data Protection Regulation: still a sound system for the protection of individuals? Computer Law & Security Review. 2016;32:179–94.

  56. Horwitz MJ. The transformation of American law, 1870–1960: The crisis of legal orthodoxy. Oxford: Oxford University Press; 1992.

  57. Westin AF. Privacy and freedom. New York: Atheneum Books; 1963.

    Google Scholar 

  58. Westin AF. (dir.). Computers, personnel administration, and citizen rights. 1979. 500 (50), US Department of Commerce, National Bureau of Standards.

  59. Warren SD, Brandeis LD. The right to privacy. Harvard Law Review. 1890;4(5):193–220.

    Article  Google Scholar 

  60. Whitman JQ. The two Western cultures of privacy: Dignity versus liberty. Yale Law Journal. 2004;113:1151–221.

    Article  Google Scholar 

  61. Casanovas P, Palmirani M, Peroni S, van Engers T, Vitali, F. Special Issue on the Semantic Web for the Legal Domain, Guest Editors Editorial: The Next Step. Semantic Web Journal. 2016, 7 (2): 1–13. Available at: available at SSRN: http://ssrn.Com/abstract=2765912. Cited 5 October 2016.

  62. Rodríguez-Doncel V, Santos C, Casanovas P, Gómez-Pérez A. Legal aspects of linked data – The European framework, Computer Law & Security Review: The International Journal of Technology Law and Practice, 2016;32(6): 799–813, doi: 10.1016/j.clsr.2016.07.005.

  63. Pagallo U. The Law of Robots. Crime, contracts, and torts. Dordrecht: Springer; 2013.

    Google Scholar 

  64. Mittelstadt BD, Floridi L, (ed.). The Ethics of Biomedical Big Data. Dordrecht: Springer; 2016.

    Google Scholar 

  65. Werbin KC. The List Serves: Population Control and Power. Amsterdam: Institute of Network Cultures; 2008.

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Acknowledgements

This article is an outcome of Melbourne-based researchers of the Law and Policy Program of the Australian government-funded Data to Decisions Cooperative Research Centre (http://www.d2dcrc.com.au/), with the cooperation of the UAB Institute of Law and Technology (DER2012-39492-C02-01, and DER2016-78108-P).

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Correspondence to Pompeu Casanovas.

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The authors declare that they have no conflict of interest.

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Data to Decisions Cooperative Research Centre (D2D CRC Ltd., ABN 45168769677; Project DC160051 - Practical perspectives on a balanced, enabling regulatory framework for data-based decision-support technologies used by law enforcement and national security in Australia.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Informed consent was obtained from all individual participants included in the study (not applicable).

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This article is part of the Topical Collection on Privacy and Security of Medical Information

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Casanovas, P., De Koker, L., Mendelson, D. et al. Regulation of Big Data: Perspectives on strategy, policy, law and privacy. Health Technol. 7, 335–349 (2017). https://doi.org/10.1007/s12553-017-0190-6

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  • DOI: https://doi.org/10.1007/s12553-017-0190-6

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