The incorporation of Artificial Intelligence (AI) across various industries has completely transformed modern life, offering unparalleled efficiency, productivity, and convenience. Artificial intelligence has a crucial role to play in reshaping the world in profound ways ranging from healthcare to education to finance etc.[1] AI which started off as a mere technological invention but has now gained the status of an inseparable & indispensable part in human life. However, the widespread implementation of AI-driven technologies has sparked concerns regarding data privacy and potential misuse of personal data of individuals. This article delves into development of data protection since the inception of this idea.
TRADITIONAL APPROACH TO DATA PROTECTION & THE BLACK BOX THEORY
During the initial development of the concept of data protection the German Federal Courts played a pivotal role in establishing right to privacy across the European Continent which eventually spread across the globe.[2] These courts held and propagated the opinion that the owner of the data had the right to dissemination of such data i.e., such person has the right to determine who gets access to their information. An analogy was drawn between right to privacy & property rights wherein just as a property owner has the right to choose who gets the right to access & use his property in the same way in case of personal data, the concerned owner of such data is the one who gets the right to decide who gets access & right to use his personal data. According to this traditional approach every single processing of personal data, storage of such data & incorporation of such data with location for instance would also be regarded as privacy evasion.
Since the artificial intelligence possess the feature of self-learning it has the capability to process any given data & give inferences.[3] Even though AI may be transparent to its programmer but once the algorithm has processed a wide variety of data & used its self-learning capabilities multiple times in the cycle of such process even the programmer loses its capability of determining the different kinds of data & the way of their processing to reach at inferences derived by certain kind of data correlations. The aforesaid self- adaptive nature of certain AI results in lack of transparency. This phenomenon of lack of transparency is referred to as black box issue of AI which leads to traditional approach of data protection being ineffective.[4]
In contrast to this an alternative model proposed over the time debates on the view whether right to data protection is a fundamental right or not. It presents two novel and distinct perspective on the right to data protection one claiming it to be a distinct fundamental right. On the other hand, the other perspective argues for an integration of data protection within existing fundamental rights, such as privacy and freedom of expression. The second approach offers a more comprehensive framework for addressing data protection & privacy concerns, particularly in the context of advancing technologies like AI.[5]
Moreover, the model emphasizes the pre-emptive nature of data protection, distinguishing it from conventional fundamental rights. While traditional rights typically respond to actual infringements, data protection proactively addresses potential harms associated with the abstract collection and processing of personal data. This proactive stance requires justification for data collection, even in the absence of concrete evidence of misuse. Courts have historically associated the right to data protection with various fundamental rights, underscoring its interconnectedness rather than treating it as an isolated entitlement. Even in jurisdictions where data protection is explicitly guaranteed, it is often perceived as complementary to other substantive rights, reinforcing the idea of data protection as an overarching enhancement of fundamental rights.
RECENT DEVELOPMENT IN PRIVACY PRRESERVING TECHNOLOGIES
As we navigate with the evolving landscape of data privacy in the domain of artificial intelligence reconciliation of traditional data protection frameworks with emerging privacy-enhancing technologies (PETs) appears to be the need of the hour. With growing transparency concerns of AI algorithms and their impact on personal data management, recent progress in PETs presents promising avenues for addressing the privacy risks inherent in AI-driven environments.[6]
PETs comprise of a diverse array of tools and methods which have become crucial for the purpose of safeguarding personal data in today’s rapidly expanding digital world. At the heart of PETs lie cryptographic solutions, providing a robust framework for securing data during transmission and storage. Encryption, considered as the foundational stone of PETs plays the role of ensuring the confidentiality of personal & confidential data by making it unintelligible for unauthorized agents attempting to use it.[7] Further, techniques like homomorphic encryption enhance data privacy by allowing encrypted data computations as an attempt to preserve the confidentiality of the personal data which could have been violated if decrypting was required to be compulsorily carries on as a pre-requisite for computational purpose.[8] Along the same lines, secure multi-party computation (MPC) empowers multiple entities to collaborative & then after compute functions over their inputs without revealing individual data, thereby ensuring privacy even in shared environments.
Other key components of PETs include the techniques of Pseudonymization & anonymization which are allotted with essential role in safeguarding personal data at the same time preserving its usability. Anonymization adapts the methods of removing identifying information which leads to difficulty in linking data to specific individuals.[9] Pseudonymization encompasses the replacing of identifiable attributes with pseudonyms, thereby allowing legitimate usage of data without disclosing the individuals’ identity.[10] Even after strict implementation of such safeguarding procedures such as Pseudonymization & anonymization by the programmers of AI algorithms, there still persists challenges such as re-identification attacks and data linkage threats.[11]
Further, Privacy-preserving algorithms represent another significant aspect of PETs, enabling data analysis while safeguarding individuals’ privacy. Introduction of randomness into query responses is a technique which is utilized to ensure indistinguishability of individual contributions from aggregate results. Deployment of federated learning facilitates whereby the training of machine learning models takes place across distributed datasets without sharing raw data, thus attempting to mitigate privacy concerns even in collaborative settings.[12] By integrating privacy-preserving mechanisms into algorithm design, developers can create systems that balance data utility along with privacy protection.[13]
Despite these potential arrays of benefits of PETs, their adoption & implementation poses a variety of challenges and ethical considerations. Among these challenges, balancing privacy protection with data utility is a fundamental challenge, as stronger privacy measures often come at the cost of reduced data accessibility and analytical capabilities. Moreover, for successful integration of PETs into existing systems the usability, interoperability, and scalability of PETs must be ensured. Ethical concerns surrounding data governance, consent, and transparency further highlight the importance of responsible deployment and use of PETs.
Looking ahead, PETs hold high potential for shaping the future of data privacy and security. With each passing day, advances in cryptography, privacy-preserving algorithms, and data anonymization techniques continue to drive innovation in PETs, offering new & innovative solutions to ever evolving privacy concerns. Interdisciplinary collaboration between various researchers, policymakers, and industry stakeholders will be crucial for advancement of PETs & at the same time addressing the complex interplay between technology, regulation, and societal values.
In conclusion, privacy-enhancing technologies have an essential role to play for the purpose of safeguarding personal data in the current digital era which is rapidly evolving. By harnessing the cryptographic methods, Pseudonymization, anonymization and privacy-preserving algorithms, PETs offer pioneering solutions to the problem data protection & privacy at the same time leaving the scope of data-driven innovation open to the world. However, their adoption requires careful consideration of technical, ethical, and regulatory factors. As technology evolves, ongoing research and collaboration will be key to advancing PETs and ensuring that privacy remains a fundamental right in the digital age.
Notes
[1] https://timesofindia.indiatimes.com/readersblog/mywriteexpress/artificial-intelligence-unleashing-the-power-of-innovation-56241/.
[2] https://www.edps.europa.eu/data-protection/data-protection/legislation/history-general-data-protection-regulation_en.
[3] https://www.red-gate.com/simple-talk/development/data-science-development/introduction-to-artificial-intelligence/.
[4] Surden, H. (2021). Machine learning and law: An overview. Research Handbook on Big Data Law, 171-184.
[5] Poscher, R. (2021). Artificial intelligence and the right to data protection.
[6] https://economictimes.indiatimes.com/news/how-to/ai-and-privacy-the-privacy-concerns-surrounding-ai-its-potential-impact-on-personal-data/articleshow/99738234.cms?from=mdr.
[7] https://unesdoc.unesco.org/ark:/48223/pf0000246527.
[8] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098691/.
[9] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6502465/.
[10] https://www.enisa.europa.eu/publications/pseudonymisation-techniques-and-best-practices/@@download/fullReport.
[11] https://mostly.ai/blog/synthetic-data-protects-from-ai-based-re-identification-attacks.
[12] Qiang Yang et al., “Federated Machine Learning: Concept and Applications,” ACM Transactions on Intelligent Systems and Technology 10, no. 2 (2019): 12:1-12:19. https://doi.org/10.1145/3298981.
[13] Timan, T., & Mann, Z. (2021). Data protection in the era of artificial intelligence: trends, existing solutions and recommendations for privacy-preserving technologies. In The elements of big data value: Foundations of the research and innovation ecosystem (pp. 153-175). Cham: Springer International Publishing.
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Author: Palak Singh, 4th year student from the Institute of Law, Nirma University, Ahmedabad.
