The future of privacy in a world dominated by open data

In the era dominated by artificial intelligence that we are just beginning, open data has rightfully become an increasingly valuable asset, not only as a support for transparency but also for the progress of innovation and technological development in general.

The opening of data has brought enormous benefits by providing public access to datasets that promote government transparency initiatives, stimulate scientific research, and foster innovation in various sectors such as health, education, agriculture, and climate change mitigation.

However, as data availability increases, so does concern about privacy, as the exposure and mishandling of personal data can jeopardize individuals' privacy. What tools do we have to strike a balance between open access to information and the protection of personal data to ensure people's privacy in an already digital future?

Anonymization and Pseudonymization

To address these concerns, techniques like anonymization and pseudonymization have been developed, although they are often confused. Anonymization refers to the process of modifying a dataset to eliminate a reasonable probability of identifying an individual within it. It is important to note that, in this case, after processing, the anonymized dataset would no longer fall under the scope of the General Data Protection Regulation (GDPR). This data.gob.es report analyzes three general approaches to data anonymization: randomization, generalization, and pseudonymization.

On the other hand, pseudonymization is the process of replacing identifiable attributes with pseudonyms or fictitious identifiers in a way that data cannot be attributed to the individual without using additional information. Pseudonymization generates two new datasets: one containing pseudonymized information and the other containing additional information that allows the reversal of anonymization. Pseudonymized datasets and the linked additional information fall under the scope of the General Data Protection Regulation (GDPR). Furthermore, this additional information must be separated and subject to technical and organizational measures to ensure that personal data cannot be attributed to an individual.

Consent

Another key aspect of ensuring privacy is the increasingly prevalent "unambiguous" consent of individuals, where people express awareness and agreement on how their data will be treated before it is shared or used. Organizations and entities that collect data need to provide clear and understandable privacy policies, but there is also a growing need for more education on data handling to help people better understand their rights and make informed decisions.

In response to the growing need to properly manage these consents, technological solutions have emerged that seek to simplify and enhance the process for users. These solutions, known as Consent Management Platforms (CMPs), originally emerged in the healthcare sector and allow organizations to collect, store, and track user consents more efficiently and transparently. These tools offer user-friendly and visually appealing interfaces that facilitate understanding of what data is being collected and for what purpose. Importantly, these platforms provide users with the ability to modify or withdraw their consent at any time, giving them greater control over their personal data.

Artificial Intelligence Training

Training artificial intelligence (AI) models is becoming one of the most challenging areas in privacy management due to the numerous dimensions that need to be considered. As AI continues to evolve and integrate more deeply into our daily lives, the need to train models with large amounts of data increases, as evidenced by the rapid advances in generative AI over the past year. However, this practice often faces profound ethical and privacy dilemmas because the most valuable data in some scenarios is not open at all.

Advancements in technologies like federated learning, which allows AI algorithms to be trained through a decentralized architecture composed of multiple devices containing their own local and private data, are part of the solution to this challenge. This way, explicit data exchange is avoided, which is crucial in applications such as healthcare, defense, or pharmacy.

Additionally, techniques like differential privacy are gaining traction. Differential privacy ensures, through the addition of random noise and mathematical functions applied to the original data, that there is no loss of utility in the results obtained from the data analysis to which this technique is applied.

Web3

But if any advancement promises to revolutionize our interaction on the internet, providing greater control and ownership of user data, it would be web3. In this new paradigm, privacy management is inherent in its design. With the integration of technologies like blockchain, smart contracts, and decentralized autonomous organizations (DAOs), web3 seeks to give individuals full control over their identity and all their data, eliminating intermediaries and potentially reducing privacy vulnerabilities.

Unlike current centralized platforms where user data is often "owned" or controlled by private companies, web 3.0 aspires to make each person the owner and manager of their own information. However, this decentralization also presents challenges. Therefore, while this new era of the web unfolds, robust tools and protocols must be developed to ensure both freedom and privacy for users in the digital environment.

Privacy in the era of open data, artificial intelligence, and web3 undoubtedly requires working with delicate balances that are often unstable. Therefore, a new set of technological solutions, resulting from collaboration between governments, companies, and citizens, will be essential to maintain this balance and ensure that, while enjoying the benefits of an increasingly digital world, we can also protect people's fundamental rights.

Content prepared by Jose Luis Marín, Senior Consultant in Data, Strategy, Innovation & Digitalization.

The content and views reflected in this publication are the sole responsibility of the author.