Every organisation that wants to share data faces the same dilemma. Combining information with partners can unlock valuable insights — detecting fraud across banks, improving medical research across hospitals or training better AI models. But sharing sensitive data creates privacy, security and regulatory risks that many organisations are unwilling to accept.
Silence Laboratories, a Palo Alto-based cybersecurity startup built on deep cryptography research, is trying to resolve that dilemma. Its technology lets organisations work with sensitive data without exposing the underlying information, according to a profile published by YourStory on Wednesday, September 23.
“Our products empower organisations to compute on encrypted and distributed data sources, without ever exposing the data to any of the parties involved,” chief executive and co-founder Jay Prakash told YourStory.
The founders and the team
Silence Laboratories was founded by Jay Prakash, chief technology officer Andrei Bytes and chief security officer Tony Quek. The company has a 30-member team working across 14 countries, according to YourStory.
The company’s roots lie in academic research. Its founders earned doctorates at the Singapore University of Technology and Design, where the company was incubated, and it emerged from more than a decade of research and development in applied cryptography and application security, according to earlier reports on the company.
From silent signals to cryptography
The company’s name reflects its origins. Its early work focused on proximity-based authentication using a fusion of sound waves and radio-frequency signals. “The idea was to use sound and wireless signals to confirm that a login device and a token device are physically close to each other when authentication is approved,” Prakash told YourStory.
That early work with silent, inaudible signals gave the startup its name. As the company expanded into a broader suite of privacy-enhancing and cryptographic products, the name stayed, even as the business pivoted towards cryptographic security.
How multi-party computation works
At the heart of Silence Laboratories’ technology are two cryptographic techniques: multi-party computation (MPC) and zero-knowledge proofs.
Multi-party computation allows several parties to jointly compute a result using their combined data, without any party revealing its own data to the others. For example, several banks could calculate whether a customer appears on multiple fraud watchlists without any bank disclosing its list to the others. Each party retains control of its own information, and only the agreed result is revealed.
Zero-knowledge proofs allow one party to prove that a statement is true without revealing the information behind it — for instance, proving that a person is over a certain age without disclosing their date of birth.
Together, these techniques replace reliance on trusted third parties with cryptographic guarantees. Instead of trusting a central intermediary to hold everyone’s data securely, organisations can rely on mathematics to ensure that sensitive information is never exposed.
Research as a differentiator
Silence Laboratories competes with companies such as Fireblocks and Inpher, according to YourStory. Prakash says the company differentiates itself through its focus on research. Its work has been published and presented at leading security and cryptography forums, including IEEE Security and Privacy, the ACM Conference on Computer and Communications Security and EuroCrypt.
The company has translated that research into production systems, including a post-quantum MPC protocol, according to YourStory. Prakash said its products are production-ready, require no specialised hardware and keep data encrypted.
The post-quantum dimension is increasingly important. Future quantum computers could break widely used encryption schemes, prompting governments and companies to begin migrating to quantum-resistant cryptography. Privacy technologies designed with post-quantum security in mind could be better positioned for long-term enterprise adoption.
Funding and expansion plans
Silence Laboratories has raised $6 million in funding and is planning to raise more to support its expansion, according to YourStory. Prakash said the capital would primarily go towards hiring talent across cryptography, engineering and go-to-market functions, as well as expanding into new markets.
In February 2024, the company announced a $4.1 million round co-led by Pi Ventures and Kira Studio, with participation from angel investors, TechCrunch reported at the time. Pi Ventures said then that secure data collaboration was a growing problem, especially in highly regulated domains such as finance and healthcare.
Enterprise buyers increasingly ask whether such systems can run at the speed their operations demand. Performance has historically been one of the main barriers to deploying MPC outside specialised settings, which is why claims of production-readiness without specialised hardware are central to the company’s pitch.
A distributed team spanning 14 countries also reflects how deeptech startups now recruit globally for scarce cryptography talent rather than concentrating in a single hub.
Where the demand is
The use cases for privacy-preserving computation span several industries. In financial services, banks and payment companies can collaborate on fraud detection and anti-money-laundering checks without sharing customer data. In healthcare, institutions can combine patient data for research while preserving privacy. In digital assets, MPC has become a standard approach for securing cryptocurrency wallets by splitting private keys across multiple parties.
AI adds another dimension. Organisations want to train and run AI models on sensitive data, but are wary of exposing that data to model providers or partners. Privacy-enhancing technologies could allow AI to be applied to confidential information without compromising it.
An India connection
For India, Silence Laboratories illustrates the global reach of Indian-origin deeptech founders. It also speaks to a policy question India is grappling with: the Digital Personal Data Protection Act imposes stricter obligations on how personal data is processed, while India’s digital public infrastructure depends on data sharing across institutions. Technologies that enable privacy-preserving data collaboration could help reconcile those goals.
The challenge ahead
Privacy-enhancing technologies have long been praised by researchers but have struggled to achieve mainstream enterprise adoption because of complexity, performance overheads and limited awareness among buyers. Companies such as Silence Laboratories must show that their tools are fast, practical and easy to integrate — and that they deliver clear business value.
If they succeed, the payoff could be significant. As data regulation tightens and AI increases the value of combining information, the ability to compute on data without exposing it may shift from an academic curiosity to a core part of enterprise infrastructure.
