went into @Nillion 's zkTLS research thinking proving web data was authentic was the hard part. Then I realised there's another question:
What happens AFTER proving the data is real?
For a loan, your bank has your transactions, a platform has your income, and a credit provider has your credit history.
zkTLS can prove facts about this data without revealing details. But what if an application needs all three sources to calculate your eligibility?
Nillion's April 2025 research explores combining authenticated web data with blind computation. zkTLS helps verify a data source and detect tampering, while blind computation processes information from multiple TLS connections without exposing raw inputs.
Privacy and provenance solve different problems. Blind computation protects data during processing, but doesn't prove it's truthful. Authentic data isn't always safe to reveal or combine.
Imagine checking loan eligibility using verified bank, income and credit data, then returning the result without exposing your entire financial life.
Hmm. Nillion presents this as a research direction. The guarantees depend on how authentication, computation and verification are implemented.
We've spent years either trusting platforms with private information or exposing it to use it.
Could zkTLS and blind computation bridge the gap between proving private data is authentic and putting it to work?
Or is the challenge getting applications to change how they handle information?
#NIL $NIL
What happens AFTER proving the data is real?
For a loan, your bank has your transactions, a platform has your income, and a credit provider has your credit history.
zkTLS can prove facts about this data without revealing details. But what if an application needs all three sources to calculate your eligibility?
Nillion's April 2025 research explores combining authenticated web data with blind computation. zkTLS helps verify a data source and detect tampering, while blind computation processes information from multiple TLS connections without exposing raw inputs.
Privacy and provenance solve different problems. Blind computation protects data during processing, but doesn't prove it's truthful. Authentic data isn't always safe to reveal or combine.
Imagine checking loan eligibility using verified bank, income and credit data, then returning the result without exposing your entire financial life.
Hmm. Nillion presents this as a research direction. The guarantees depend on how authentication, computation and verification are implemented.
We've spent years either trusting platforms with private information or exposing it to use it.
Could zkTLS and blind computation bridge the gap between proving private data is authentic and putting it to work?
Or is the challenge getting applications to change how they handle information?
#NIL $NIL