Artificial intelligence (AI) has become a powerful tool in our daily lives, from chatbots that help answer questions to algorithms that assist in diagnosing medical conditions. However, with the rise of AI-powered tools comes a pressing question: How do we ensure that our sensitive information stays private and secure?
Our team at Nillion is developing ways to make large AI models work without compromising user privacy. In this post, we’ll explore the practical uses of these privacy-preserving AI advancements and explain why they matter for individuals, businesses, and industries alike.
Why Privacy Matters in AI
When we interact with AI—whether through chatbots, voice assistants, or recommendation engines—these systems are often trained on or process highly personal data. Imagine if your AI assistant could access your personal calendar, medical history, and even preferences to better serve you. While this could enable a highly customized, helpful experience, the privacy stakes are extremely high. If such sensitive information were leaked, the consequences could be severe. Privacy-preserving technologies ensure that even as AI becomes more personalized, our personal information stays protected.
Data protection laws and consumer expectations are pushing for better safeguards. Major companies are restricting employee use of AI tools over concerns of data leaks, underscoring a significant industry shift toward prioritizing privacy. However, if we want to keep unlocking AI’s potential without compromising our data, privacy-preserving technologies are essential. Privacy-preserving AI allows us to unlock the full potential of these personalized assistants without putting private data at risk.
A vision of AI as a truly personal assistant means it would understand and act on our behalf, much like a digital butler. For example, imagine an AI that:
- Knows your medical history and helps schedule appointments, even reminding your doctor about specific concerns.
- Manages your finances by tracking spending patterns, anticipating bill payments, and making budgeting suggestions.
- Coordinates travel arrangements by accessing your preferences, from your favorite airline to dietary needs at hotels.
To be truly effective, a personalized AI assistant would need access to a vast amount of sensitive data. However, this kind of deep personalization also demands the highest standards of privacy. Privacy-preserving AI allows for this depth of customization by keeping personal data private and secure, enabling a future where our AI assistants can truly work for us—without compromising our privacy.
At a high level, privacy-preserving AI allows two or more parties to use AI models collaboratively without exposing their private data. In a prominent use case, one company owns a private model and another company owns private data (e.g., medical records) but neither can reveal its proprietary data to the other. Using secure multiparty computation, the two companies can perform privacy-preserving AI inference without sacrificing the security of their data. We delve into the inner workings of secure multiparty computation for privacy-preserving AI in our latest blog post.¹
Privacy-Preserving AI in Practice
Let’s look at some more concrete examples of how privacy-preserving AI can reshape a variety of fields in ways that respect data privacy:
- Secure Collaboration for Cybersecurity: Companies are constantly working to secure their software and systems. Through privacy-preserving AI, cybersecurity firms can use shared models to scan for vulnerabilities in code without exposing sensitive software information. This collaborative approach lets companies enhance security together without compromising proprietary code. View this use case interactively in the first video of our Curl blog post.¹
- Healthcare Diagnostics: Imagine a healthcare provider using AI to analyze patient data and improve diagnosis accuracy. Privacy-preserving AI lets hospitals process sensitive health records securely, allowing doctors to leverage AI for diagnostics without risking patient privacy. This opens up avenues for personalized treatments and research while complying with strict regulations like HIPAA in the U.S. and GDPR in Europe.
- Financial Services and Risk Assessment: Financial institutions can harness the power of AI to assess credit risk or detect fraud without exposing clients’ financial histories. Privacy-preserving AI lets banks and lending companies process private financial data securely, allowing them to use powerful predictive models while safeguarding customer information. This helps financial services better manage risk and improve lending accuracy without data privacy risks.
- Data Protection in AI-Powered Customer Service: Many customer service platforms use AI to help answer questions or address issues. Privacy-preserving AI lets these platforms deliver accurate, helpful responses while safeguarding customer data. This is particularly important in fields like telecom or e-commerce, where the data handled includes personal details and preferences.
And the list goes on!
At Nillion, we are developing a platform called AIVM that makes it possible to use large AI models in a privacy-preserving way.² AIVM allows uploading a) sensitive AI models in a privacy-preserving way, and b) private data for performing AI inference on the uploaded models securely. This means that businesses, researchers, and developers can work with powerful AI models without ever risking their sensitive data. AIVM builds upon our research paper called Curl which was published at the Conference on Applied Machine Learning in Information Security (CAMLIS) 2024.
Privacy-preserving AI isn’t just about protecting data—it’s about building trust. As AI becomes a greater part of our lives, especially with more personal and sensitive use cases, people need assurances that their private data won’t be misused or exposed. Industries that embrace privacy-preserving AI early will set a new standard for responsible, trustworthy AI. With privacy-preserving AI in place, we can then envision a personalized AI that acts on our behalf is only possible with privacy protections that keep data secure. Privacy-preserving AI lets us build a future where powerful, personalized AI enhances our lives without compromising our privacy.
¹ https://nillion.com/news/1175
² https://docs.nillion.com/aivm
Manuel B. Santos, Dimitris Mouris, Mehmet Ugurbil, Stanislaw Jarecki, José Reis, Shubho Sengupta, and Miguel de Vega.
³ Curl: Private LLMs through Wavelet-Encoded Look-Up Tables.
In Conference on Applied Machine Learning for Information Security (CAMLIS), 2024.
PDF: https://eprint.iacr.org/2024/1127.pdf
Code: https://github.com/jimouris/curl