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Transnational Flow of Personal Data in Home and Office Devices

Received: 18 May 2022    Accepted: 9 June 2022    Published: 18 July 2022
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Abstract

The world is embracing Artificial Intelligence (AI) at a rapid rate, and over the forthcoming decade it is likely to pervade the daily lives of everybody. Countries are developing, embracing and adopting AI at varying rates, some more rapidly than others. On the one hand, the application of AI in managing the smart home infrastructure will pave the way for personal data to be gathered from the automated devices. It will be that advanced, the technology will be able to predict user behaviour, provide maintenance data, help enhance data security and privacy. This can be achieved, by connecting devices throughout the homes by many different devices. Nonetheless, as people begin to adopt new technology in the home, or otherwise known as Smart Home technology (robots, televisions, fridges, toys etc.), the access to personal data will be on an unprecedented level. Conversely, the privacy intrusions may out-weigh the benefits of the technology. Apart from the social and economic benefits that AI will bring into the home, it will have it downsides. There is an emerging debate as to the safety of personal data, and privacy from these devices. In other words, what has emerged is the notion of dataveillance or behavioural data that, is able to detect and store specific data on and individual, enabling others to learn intimate knowledge of actions, moods and expression, amongst others. Arguably, some of the most vulnerable cohorts will be children, the disabled and elderly, from the use of this technology, and the personal data that entities are able to capture, and subsequently use for financial gain. Furthermore, problematic is the development of ‘behavioural data’. Behavioural data, has the ability to create significant bias based on race, ethnicity, religion, disability and language. Put another way, behavioural data has many similarities to dataveillance. This paper will briefly highlight how transnational data flows from the devices have the potential to create and restrict competition. The paper further confirms that a recent study in 2021, demonstrated that as this economic activity (data flows) grows there are increasing security concerns and issues that expose personal and commercial data. Further research is needed to reconcile the law with the technology, to ensure data flows are providing their intended economic benefits, with that of protecting the personal data captured and used by in home and office devices.

Published in International Journal of Intelligent Information Systems (Volume 11, Issue 3)
DOI 10.11648/j.ijiis.20221103.12
Page(s) 39-50
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Privacy, Data Flows, Smart Home-Office Devices, Law

References
[1] Graham Greenleaf, Global data privacy in a networked world, in Brown, I (ed) Research Handbook on Governance of the Internet Cheltenham: Edward Elgar, (2012). p 1.
[2] Roger Clarke, Graham Greenleaf, Dataveillance Regulation: A Research Framework, Journal of Law and information Science, 25, 1 (2018).
[3] Robert Walters, Marko Novak, Artificial Intelligence, Data Protection, Cyber Security and the Law, Springer (2021).
[4] Leon Trakman, Robert Walters, Cross Border Insolvency and Restructuring, Routledge, forthcoming.
[5] Mapping the challenges and opportunities of artificial intelligence for the conduct of diplomacy, Diplo Foundation Ministry of Foreign Affairs Finland, https://www.diplomacy.edu/AI-diplo-report
[6] Robert Walters, Matthew Coghlan, Data Protection and Artificial Intelligence Law: Europe Australia Singapore - An Actual or Perceived Dichotomy, American Journal of Science, Engineering and Technology 2019; 4 (4): 55-65. This paper does draw in elements of this earlier work by Walters and Coghlan.
[7] Phillip Jackson, Introduction To Artificial Intelligence 1, Dover Publ’n, Inc., 2d ed. (1974), 192-338.
[8] World Intellectual Property Organization Technology Trends, Artificial Intelligence, https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf
[9] Law library of Congress, Regulation of Artificial Intelligence in Selected Jurisdictions, January 2019, https://www.loc.gov/law/help/artificial-intelligence/regulation-artificial-intelligence.pdf
[10] Ibid, John S. McCain National Defense Authorization Act for Fiscal Year 2019, Pub. L. 115-232, § 238, 132 Stat. 1658 (2018), https://www.congress.gov/115/bills/hr5515/BILLS-115hr5515enr.pdf.
[11] Ibid, FAA Reauthorization Act of 2018, Pub. L. 115-254, § 548, 132 Stat. 3186, https://www.congress.gov/115/bills/ hr302/BILLS-115hr302enr.pdf.
[12] AI HLEG, A Definition of AI: Main Capabilities and Scientific Disciplines (2018), https://ec.europa.eu/newsroom/dae/document.cfm?doc_id=56341
[13] Mihalis Kritikos, European Parliamentary Research Service Scientific Foresight Unit (STOA) PE 634.427 – March 2019, https://www.europarl.europa.eu/at-your-service/files/be-heard/religious-and-non-confessional-dialogue/events/en-20190319-artificial-intelligence-ante-portas.pdf
[14] Robert Walters, Marko Novak, Cyber Security, Artificial Intelligence, Data Protection, and the Law, Springer (2021).
[15] Samuel D. Warren and Louis D. Brandeis, The Right to Privacy, Harvard Law Review, IV (5), (1890), 195.
[16] Judith Jarvis Thomson, The Right to Privacy, Philosophy & Public Affairs, Vol. 4 No. 4 (1975), 295.
[17] Shoshana Zuboff, Big Other: Surveillance Capitalism and The Prospect of an Information Civilization, Journal of Information Technology (2015). Palgrave Macmillan. 76.
[18] In quotes: Lee Kuan Yew, 2015, https://www.bbc.com/news/world-asia-31582842
[19] European Convention for the Protection of Human Rights 1950 https://www.echr.coe.int/documents/convention_eng.pdf
[20] Warren and Brandeis, op.cit, p. 213.
[21] Robert C. Post. Three Concept of Privacy. The Georgetown Law Journal, Vol. 89, (2001), 2088.
[22] Jeffrey Rosen, The Unwanted Gaze: The Destruction of Privacy in America, 2000. Vintage Book. 7.
[23] Daniel J. Solove, A Taxonomy of Privacy, University Pennsylvania Law Review, (2006) 488.
[24] Daniel J Solove, Conceptualizing of Privacy, California Law Review, 90 (4), (2002), 1092.
[25] Jon L. Mills, The Lost Right. New York: Oxford University Press, (2008), 14.
[26] Graham Greenleaf, Global Data Privacy Laws 2019: 132 National Laws & Many Bills 157 Privacy Laws & Business International Report, (2019) 14-18.
[27] Alam, Mohammad Arif Ul; ROY, Nirmalya; MISRA, Archan; and TAYLOR, Joseph. CACE: Exploiting behavioral interactions for improved activity recognition in multi-inhabitant smart homes. (2016). 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS): Nara, Japan, June 27-30: Proceedings. 539-548. Research Collection School of Information Systems.
[28] Muhammad Habibur Rehman, Chee Sun Liew, Teh Ying Wah, Junaid Shuja and Babak Daghighi, Mining Personal Data Using Smartphones and Wearable Devices: A Survey, Faculty of Computer Science and Information Technology, 2015-15, 4431-4440.
[29] United States Patent, No. 7, 930, 197, B2. https://patentimages.storage.googleapis.com/ee/8f/2c/0bd80a64ef6a52/US7930197.pdf
[30] Robert Walters, Leon Trakman, Bruno Zeller, (2019) Data Protection Law: A Comparative Analysis of Asia-Pacific and European Approaches, Springer.
[31] Graham Greenleaf, Thematic: Technology and the Professions, UNSW Law Journal Volume 40 (1) (2017), 310.
[32] De Hert p, Gutwirth S, (2006) Privacy, Data Protection and Law Enforcement. Opacity of the Individual and Transparency of Power, in Claes E, Duff A, Gutwirth S, Privacy and the Criminal Law, Antwerp-Oxford, Intersentia, pp. 61-104, in Robert Walters, Leon Trakman, Bruno Zeller, (2019) Data Protection Law: A Comparative Analysis of Asia-Pacific and European Approaches, Springer.
[33] International Organisation for Standardisation/IEC 2382-1-1993 and its successors.
[34] Kokott J, Sobotta C, The distinction between privacy and data protection in the jurisprudence of the CJEU and the ECtHR, International Data Privacy Law, Oxford University Press, vol 3, Issue 4, (2013), 222–228, in Robert Walters, Leon Trakman, Bruno Zeller, (2019) Data Protection Law: A Comparative Analysis of Asia-Pacific and European Approaches, Springer.
[35] See: Isaac Asimov, I Robot, Round Around, Street & Smith Publication. (1942), 9.
[36] Momayya Madakam, R. Ramaswamy, Siddharth Tripathi, Internet of Things (IoT): A Literature Review, 2015. 165.
[37] See: Defra, Delivering the Benefits of Smart Appliances. London: Department for Environment, Food and Rural Affairs, (2017), 10.
[38] L. Atzori, et al., The Internet of Things: A Survey. Comput. Netw. doi: 10.1016/j.comnet.2010.05.010. (2010), 19.
[39] PWC. (2017) Smart Home, Seamless Life Unlocking a Culture of Convenience. Paris: Price Waterhouse Coopers France, 22, https://www.pwc.fr/fr/assets/files/pdf/2017/01/pwc-consumer-intelligence-series-iot-connected-home.pdf.
[40] RiskBased Security, (2019). Data Breach Quick View Report 2019, https://pages.riskbasedsecurity.com/hubfs/Reports/2019/Data%20Breach%20QuickView%20Report%202019%20Q3%20Trends.pdf.
[41] Mustafa A. Mustafa, Sara Cleemput, Abdelrahaman Aly, and Aysajan Abidin, Secure and Privacy-preserving Protocol for Smart Metering Operational Data Collection. IEEE Transactions on Smart Grid. (January 2018), p. 2.
[42] Benjamin Fabian, Tobias Feldhaus, Privacy-Preserving Data Infrastructure for Smart Home Appliances Based on the Octopus DHT. Computers In Industry, (2014), 3.
[43] Marlen Bissaliyev. IoT: Security and Privacy in Future Home appliances. International Journal of Applied Engineering Research, 12 (20), (2017), 10455.
[44] Jae-young, L. Analysis and Responsive Measure of Smart Home Security Threat in IoT. International journal of criminal study, 4 (1), (2019), 5.
[45] Menachem Domb, Smart Home Systems Based on Internet of Things, IoT and Smart Home Automation Intechopen, p 5-8, https://cdn.intechopen.com/pdfs/65877.pdf
[46] Datta T, Apthorpe N, Feamster N. Developer-friendly library for smart home IoT privacy-preserving traffic obfuscation, IoT S&P 18. In: Proceedings of the 2018 Workshop on IoT Security and Privacy. ACM; (2018) pp 43-48.
[47] Silpa Krishnan, Anjana M. S., Sethuraman N. Rao Security Considerations for IoT in Smart Buildings Amrita Center for Wireless Networks & Applications (AmritaWNA), 2017 IEEE International Conference on Computational Intelligence and Computing Research (2017), p 3.
[48] Mourade Azrour, Jamal Mabrouki, Azidine Guezzaz, Ambrina Kanwal, Internet of Things Security: Challenges and Key Issues, Security and Communications Networks, Volume 2021 |Article ID 5533843, https://doi.org/10.1155/2021/5533843, (2021), p 1.
[49] Benjamin Cheatham, Kia Javanmardian, Hamid Samandri, McKinsey, https://www.mckinsey.com/business-functions/quantumblack/our-insights/confronting-the-risks-of-artificial-intelligence
[50] Qualetics, How AI & IoT Are Driving Intelligence & Automation in Smart Homes, https://qualetics.com/how-ai-iot-is-driving-intelligence-automation-in-smart-homes/
[51] Remesh Tamachandran, How Artificial Intelligence Is Countering Data Protection Challenges Facing Organizations AI technology can help enterprises in endpoint security, data privacy and against phishing, malware and ransomware attacks. https://www.entrepreneur.com/article/343267.
Cite This Article
  • APA Style

    Robert Walters, Sinta Dewi Rosadi. (2022). Transnational Flow of Personal Data in Home and Office Devices. International Journal of Intelligent Information Systems, 11(3), 39-50. https://doi.org/10.11648/j.ijiis.20221103.12

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    ACS Style

    Robert Walters; Sinta Dewi Rosadi. Transnational Flow of Personal Data in Home and Office Devices. Int. J. Intell. Inf. Syst. 2022, 11(3), 39-50. doi: 10.11648/j.ijiis.20221103.12

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    AMA Style

    Robert Walters, Sinta Dewi Rosadi. Transnational Flow of Personal Data in Home and Office Devices. Int J Intell Inf Syst. 2022;11(3):39-50. doi: 10.11648/j.ijiis.20221103.12

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  • @article{10.11648/j.ijiis.20221103.12,
      author = {Robert Walters and Sinta Dewi Rosadi},
      title = {Transnational Flow of Personal Data in Home and Office Devices},
      journal = {International Journal of Intelligent Information Systems},
      volume = {11},
      number = {3},
      pages = {39-50},
      doi = {10.11648/j.ijiis.20221103.12},
      url = {https://doi.org/10.11648/j.ijiis.20221103.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20221103.12},
      abstract = {The world is embracing Artificial Intelligence (AI) at a rapid rate, and over the forthcoming decade it is likely to pervade the daily lives of everybody. Countries are developing, embracing and adopting AI at varying rates, some more rapidly than others. On the one hand, the application of AI in managing the smart home infrastructure will pave the way for personal data to be gathered from the automated devices. It will be that advanced, the technology will be able to predict user behaviour, provide maintenance data, help enhance data security and privacy. This can be achieved, by connecting devices throughout the homes by many different devices. Nonetheless, as people begin to adopt new technology in the home, or otherwise known as Smart Home technology (robots, televisions, fridges, toys etc.), the access to personal data will be on an unprecedented level. Conversely, the privacy intrusions may out-weigh the benefits of the technology. Apart from the social and economic benefits that AI will bring into the home, it will have it downsides. There is an emerging debate as to the safety of personal data, and privacy from these devices. In other words, what has emerged is the notion of dataveillance or behavioural data that, is able to detect and store specific data on and individual, enabling others to learn intimate knowledge of actions, moods and expression, amongst others. Arguably, some of the most vulnerable cohorts will be children, the disabled and elderly, from the use of this technology, and the personal data that entities are able to capture, and subsequently use for financial gain. Furthermore, problematic is the development of ‘behavioural data’. Behavioural data, has the ability to create significant bias based on race, ethnicity, religion, disability and language. Put another way, behavioural data has many similarities to dataveillance. This paper will briefly highlight how transnational data flows from the devices have the potential to create and restrict competition. The paper further confirms that a recent study in 2021, demonstrated that as this economic activity (data flows) grows there are increasing security concerns and issues that expose personal and commercial data. Further research is needed to reconcile the law with the technology, to ensure data flows are providing their intended economic benefits, with that of protecting the personal data captured and used by in home and office devices.},
     year = {2022}
    }
    

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Author Information
  • Victoria Law School, Victoria University, Melbourne, Australia

  • Faculty of Law, University of Padjajaran, Bandung, Indonesia

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