Boston, Massachusetts (USA) — 09/05/2024
In today’s digital age, the lack of robust data laws has led to rampant illegal data abuse and a “chilling effect” on legitimate data use. This situation calls for urgent regulations on data usage (National Telecommunications and Information Administration, 2024). Issues such as “big data price discrimination” (Zhao, 2020) and “spam advertising” not only infringe upon the rights of data subjects but also significantly disturb people’s lives and social order (Act-On, 2017).
Due to ambiguous legal frameworks, many companies possess valuable data but are unable to engage in lawful transactions.For instance, telecommunications providers hold vast amounts of personal data but prefer to let this data “sleep” in storage rather than develop and utilize it to provide data products and services for various industries, resulting in a significant waste of data resources. In response to these challenges, the concept of data trusts has gained traction in recent.
What are Data Trusts?
Data trusts are systems designed to address the imbalanced rights and obligations between data subjects and data controllers. They aim to ensure the privacy and security of individuals while unlocking the value of data as an asset, returning this value to the data providers, who are the trustees (Delacroix & Lawrence, 2019). The concept, first proposed by Lilian Edwards in 2004, gained wider recognition in 2021 when MIT Technology Review included data trusts in its “10 Breakthrough Technologies 2021” (MIT Technology Review, 2021).
There are currently two main types of data trusts: the “British Model” and the “American Model.” The “British Model” involves setting up an independent third party as the trustee, while the “American Model” has the data controller act as the trustee. The core idea is to establish a trust relationship between individual data rights holders and data controllers, using personal data rights as the trust subject to protect the rights of individuals in a vulnerable position. The key difference is that the “British Model” introduces an independent third-party institution as the trustee, while the “American Model” appoints the data controller as the trustee.
Practical Applications and Challenges
In practice, the “British Model” of data trusts is applied to managing citizens’ personal and health data privacy in smart cities. For example, in 2018, Alphabet’s subsidiary Sidewalk Labs proposed using a civic data trust to manage data collected from its smart city project in the Toronto area.In 2019, Sidewalk Toronto incorporated civic data trusts as part of its smart city development.
Beyond legal issues related to the definition and ownership of personal and property rights in data trusts, a critical issue in both the British and American models is how to effectively distribute benefits while avoiding the risks of trustees’ unethical behavior and encouraging trustees to actively increase the earnings of the data trust (Milne et al., 2021).
Currently, both models use legal regulations to mitigate risks and incentivize positive actions by trustees. However, proving the legality and compliance of their asset management actions is challenging, presenting significant loopholes.
Aloega: A New Approach
To address these core issues, Aloega offers a new form of data trust that combines blockchain technology. This approach decentralizes operations to mitigate the risks of unethical actions by trustees while providing incentives for positive behavior.
Aloega, as a Web 3.0 product for medical data, addresses these challenges through the following process:
Data Security and Anonymity: Aloega employs blockchain encryption and zero-knowledge proof technologies to anonymize user data, ensuring data security and anonymity. The transparency of its contract code on the blockchain prevents the misuse of non-anonymized data that could occur with traditional centralized processing. This greatly enhances user confidence in data security, potentially increasing the user base relative to traditional data trusts.
Data Processing Frameworks: For anonymized data, Aloega provides basic and customized data processing frameworks to organize, clean, and analyze data, meeting the needs of all data users and generating steady revenue. It delegates data management to the data-providing community through a DAO governance model, separating data management and processing rights to mitigate unethical risks in data transactions. The DAO governance model’s incentive and disciplinary schemes guide data managers to actively promote data products, helping the project achieve sustained positive returns.
Database Integrity and Security: Aloega ensures database integrity and security through decentralized storage and encryption technologies, protecting against data loss or theft due to data center issues.
Token Incentive Scheme: Aloega uses blockchain technology’s token incentive scheme to provide timely benefits to data providers and transforms user data into a circulating asset. This prevents trustees from exploiting information asymmetry and engaging in unethical profit-making during the trust period, creating a transparent, fair, and timely data monetization business model for data providers.
The Future of Data Trusts
The assetization of data represents an inevitable wave of the future. Under this wave, the concept of data trusts promoted by Web 2.0 is clearly lagging behind Aloega, which relies on Web 3.0 technologies. Aloega is a revolutionary product that transcends the current understanding of most users worldwide.
Aloega invites you to join us in witnessing history, disrupting the world, and creating the future together! References
Act-On. (2017, May 25). The History of Spam in Marketing. Act-On. https://act-on.com/learn/blog/the-history-of-spam-in-marketing/ Delacroix, S., & Lawrence, N. D. (2019). Bottom-up data Trusts: disturbing the “one size fits all” approach to data governance. International Data Privacy Law, 9(4). https://doi.org/10.1093/idpl/ipz014 Edwards, L. (2004, June 3). The Problem with Privacy. Social Science Research Network. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1857536 Milne, R., Sorbie, A., & Dixon-Woods, M. (2021). What can data trusts for health research learn from participatory governance in biobanks? Journal of Medical Ethics, 48(5), medethics-2020–107020. https://doi.org/10.1136/medethics-2020-107020 MIT Technology Review. (2021, February 24). 10 Breakthrough Technologies 2021. MIT Technology Review. https://www.technologyreview.com/2021/02/24/1014369/10-breakthrough-technologies-2021/ National Telecommunications and Information Administration. (2024). Chapter 1: Theory of Markets and Privacy | National Telecommunications and Information Administration. Www.ntia.gov. https://www.ntia.gov/page/chapter-1-theory-markets-and-privacy Zhao, X. (2020). Big Data and Price Discrimination. 2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA). https://doi.org/10.1109/icccbda49378.2020.9095721
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