AI needs data, while people want privacy. AI-based algorithms or models learn how to yield an output for a given input or query by being trained on a dataset without explicit programming. Therefore, the importance of developing efficient and useful AI models underscores the significance of having a vast amount of data available and accessible. On the other hand, it is also crucial to protect personally identifiable information (PII), such as names, ID numbers, or addresses, from unauthorized access or misuse during the storage, transfer, or processing of data. This raises a fundamental question: How can we leverage data to develop valuable AI models while ensuring advanced data protection?
Balancing the power of artificial intelligence with the fundamental right to privacy has become a paramount challenge in the age of AI. As AI technologies continue to advance, the collection and utilization of vast amounts of data raise significant concerns about the protection of individuals’ personal information. In this delicate balance between data-driven progress and the right to privacy, navigating ethical frameworks, legal regulations, and innovative privacy-enhancing technologies (PET) become crucial to ensuring a future where AI and individual privacy seamlessly coexist.
Understanding the Landscape: Key Data Protection Principles
Below, we briefly describe three basic principles of data protection:
- Confidentiality: Confidentiality is a fundamental principle of information security that focuses on ensuring that sensitive data is not disclosed to unauthorized individuals or entities. It involves implementing measures to protect information from being accessed or viewed by those who do not have the necessary permissions. Confidentiality safeguards sensitive data, such as personal information, financial details, or trade secrets, preventing unauthorized disclosure or exposure. Encryption is among the most common approaches for ensuring confidentiality.
- Authentication: Authentication is the process of verifying the identity of a user, system, or entity to ensure that they are who they claim to be. It involves the use of credentials, such as usernames and passwords, biometrics, or multi-factor authentication methods. By implementing strong authentication mechanisms, organizations can control access to their systems and resources, reducing the risk of unauthorized access and potential security breaches.
- Integrity: Integrity is the assurance that data remains accurate, unaltered, and consistent throughout its lifecycle. It involves protecting information from unauthorized modification, deletion, or corruption. Ensuring data integrity is crucial for maintaining the reliability and trustworthiness of information. Integrity controls, such as checksums or cryptographic hash functions, help detect and prevent unintended changes to data, providing a foundation for reliable and trustworthy systems.
One significant threat that has emerged in the age of AI is inference attacks. These attacks exploit machine learning models’ responses to deduce sensitive information about individuals, posing risks to confidentiality. In a world where AI systems are increasingly used on personal data, attackers leverage subtle patterns in model outputs to make unauthorized inferences.
Inference attacks come in various forms, including membership inference, attribute inference, and model inversion attacks. In these scenarios, adversaries analyze the model’s responses to deduce whether specific data points were part of the training set, infer attributes about individuals, or even reverse-engineer aspects of the training data. More specifically, attackers in membership inference assess the model’s behavior to determine if it responds differently to data points seen during training versus unseen data, aiming to discern whether a particular data point was part of the model’s training dataset. On the other hand, adversaries in attribute inference attacks attempt to infer private characteristics about individuals, such as gender, age, or medical conditions, from the model’s outputs. In model inversion attacks, the focus shifts to the reverse engineering of the model to extract information from it. Notice that these are among the top 10 security threats in ML identified by The Open Worldwide Application Security Project (OWASP).
Another serious threat that could be amplified by the exploitation of AI is the re-identification attack. In machine learning, a re-identification attack refers to situations where an individual’s previously anonymized or de-identified data is linked back to their actual identity. In simpler terms, it involves the process of connecting anonymous or pseudonymous data to specific individuals. This attack has already been proven possible through manual examinations of masked data by humans. Therefore, the threat becomes even more significant when realized by the power of AI, which can analyze vast amounts of data at much higher speeds.
These attacks compromise individuals’ privacy by extracting information that was not intended to be revealed during the model’s training or deployment. As AI models become more prevalent in applications like healthcare, finance, and social media, the risk of unintentional data exposure through inference attacks becomes a growing concern.
Emerging Trends and Technologies in AI Data Protection
In the fast-evolving landscape of AI, several innovative trends and technologies are playing a pivotal role in enhancing data protection and preserving privacy. Among these, edge AI, federated learning, homomorphic encryption, and differential privacy are at the forefront, ushering in a new era of secure and privacy-aware artificial intelligence.
- Edge AI: Edge AI refers to the paradigm of running AI algorithms directly on edge devices, such as smartphones, IoT devices, or local servers, rather than relying solely on centralized cloud servers. Edge AI reduces the need for sending sensitive data to the cloud, minimizing the risk of data exposure during transmission. By processing data locally, it enhances privacy and ensures that personal information remains closer to the source, reducing the potential for unauthorized access.
- Federated Learning: Federated learning is a collaborative learning approach where the model is trained across decentralized devices or servers holding local data without exchanging the raw data itself. Only model updates are shared. This technique significantly improves privacy by keeping user data on local devices, preventing the need to share raw information centrally. Users retain control over their data, and the model aggregates knowledge from various sources without compromising individual privacy.
- Homomorphic Encryption: Homomorphic encryption is a cryptographic technique that allows computations to be performed on encrypted data without decrypting it, maintaining data privacy during processing. With homomorphic encryption, AI computations can be performed on encrypted data, ensuring that sensitive information remains confidential throughout the analysis. This approach is particularly beneficial when outsourcing computations to third-party services. The main limitation of this approach is its computational overhead, making it computationally intensive and slower compared to traditional encryption methods and sometimes not feasible at all.
- Differential Privacy: Differential privacy involves adding noise or randomization to data or a model before yielding an output, making it challenging to infer individual contributions to a model’s development. It offers a robust defense against inference attacks and ensures that the model’s output does not inadvertently leak sensitive information. Moreover, techniques such as injecting noise during the training process, like perturbing model gradients, not only contribute to privacy enhancement but also result in a differentially private model that is resistant to the reverse engineering of its output.
These emerging trends and technologies collectively contribute to a more secure and privacy-preserving AI landscape. As organizations and researchers continue to explore novel approaches, the integration of these techniques represents a significant step forward in building safe AI systems that respect user privacy in the face of evolving challenges.
Data Protection Laws
The European Union (EU) is at the forefront of drafting legal regulations regarding the use of AI. The EU proposed the first EU regulatory framework for AI in April 2021, which is now called the AI Act. This is a comprehensive AI law that suggests a risk-based approach.
- Unacceptable risk: Unacceptable risk AI systems are systems considered a threat to people and will be banned. These include practices like cognitive behavioral manipulation of people (e.g., voice-activated toys that encourage dangerous behavior in children), social scoring (e.g., classifying people based on behavior), and biometric identification.
- High risk: High-risk AI systems are categorized into two groups:
- AI systems used in products under EU safety legislation, including toys, aviation, cars, medical devices, and lifts.
- AI systems in specific areas requiring registration in an EU database, including management, education, employment, law enforcement, and enjoyment of essential private and public services
All high-risk AI systems undergo assessments before market entry and throughout their lifecycle. These assessments may include issues related to data protection and privacy in AI systems.
- General purpose and generative AI: Generative AI, like ChatGPT, must comply with transparency requirements, revealing AI-generated content, preventing illegal content generation, and publishing summaries of copyrighted training data. High-impact general-purpose AI models, like GPT-4, undergo thorough evaluations with mandatory reporting of serious incidents to the European Commission.
- Limited Risk: Limited-risk AI systems, involving minimal transparency, allow users to make informed decisions and be aware when interacting with AI. This category includes systems that manipulate image, audio, or video content, such as deepfakes.
On December 9, 2023, the European Parliament reached a provisional agreement with the European Council on the AI Act. The agreed regulation will become EU law when it is formally adopted by both the Parliament and the Council.
Another recent legal action related to responsible AI is worth noting. On October 30, 2023, the White House released an Executive Order on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. The order establishes a government-wide effort to guide and support responsible AI development and deployment through federal agency leadership, regulation of industry, and engagement with international partners. The order describes eight overarching policy areas: safety and security, innovation and competition, worker support, consideration of AI bias and civil rights, consumer protection, privacy, federal use of AI, and international leadership. This executive order highlights the importance and urgency of employing PETs in AI solutions:
“Artificial Intelligence is making it easier to extract, re-identify, link, infer, and act on sensitive information about people’s identities, locations, habits, and desires. Artificial Intelligence’s capabilities in these areas can increase the risk that personal data could be exploited and exposed. […] Agencies shall use available policy and technical tools, including privacy-enhancing technologies (PETs) where appropriate, to protect privacy and to combat the broader legal and societal risks — including the chilling of First Amendment rights — that result from the improper collection and use of people’s data.
Striking the Balance between AI Innovation and Data Protection
Balancing between supporting innovations in AI and data protection has never been more critical. Achieving this is crucial for the future of AI because the unregulated evolution of AI is seen as a serious risk by some for the community. As AI continues to shape the future, the inherent need for vast datasets collides with the growing demand for data privacy. This delicate equilibrium demands novel approaches that prioritize innovation without compromising individual rights.
In this blog, we have explored the multifaceted dimensions of this challenge. We briefly touched on key data protection principles such as confidentiality, authentication, and integrity and then drew attention to new threats like inference attacks in this AI age. We then presented emerging approaches paving the way for the implementation of novel AI techniques while providing enhanced data protection. Finally, we summarized the regulations drafted by the EU for the ethical deployment and use of AI.