Discover how FHEML is revolutionizing data security, enabling encrypted computations without sacrificing performance or privacy.
Encryption has been used for thousands of years to send secret messages. One early method is the Caesar cipher, which swaps letters in the alphabet from 60 BC.
With the internet, we generate much private data, leading to more data breaches and surveillance.
To protect our data, we now use advanced methods, like end-to-end encryption, in everyday apps. But how does end-to-end encryption work, and how will it change the internet?
How your data is protected today
This is how the entire process of data encryption and decryption looks:
As soon as the data starts getting unencrypted while it’s being processed, there’s a big risk of someone hacking in and stealing it.
Why? Because it’s not kept a secret while it’s being worked on.
Some stats from the IBM team:
- 85% – The percentage of breaches that involved data stored in the cloud.
- 4.45 million USD – The global average total cost of a data breach in the year 2023.
- 277 days – A company’s average time to identify a data breach.
Cybersecurity expert Jeff Crume explains the key findings, lessons learned, and steps you can take right now to guard against data breaches and mitigate their costs.
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We need end-to-end encryption now more than ever.
But what if we could make those 277 days disappear completely? No decryption is required; The problem is already solved. And looks like FHEML is the solution.
What’s the deal with FHE and FHEML?
Fully Homomorphic Encryption — or FHE for short — is a technology that enables data to be processed without decrypting it. This means companies can offer their services without ever seeing their users’ data — and users will never notice a difference in functionality.
With data encrypted both in transit and during processing, everything we do online could now be encrypted end-to-end, not just sending messages!
Thanks to Homomorphic Encryption, you can now use your favorite online services without revealing your data.
The services don’t change from your point of view: You can use them as you always have. But from the server’s point of view, everything is encrypted—no company, government, or hacker can ever see your data.
FHEML (Fully Homomorphic Encryption + Machine Learning) combines the security benefits of FHE with the computational power of machine learning (ML).
We can apply machine learning algorithms directly to encrypted data by combining FHE and ML. We can train and use ML models on sensitive data without exposing the underlying data.
How FHEML will change the future
Preventive Medicine
Imagine knowing what you must do to stay healthy throughout your life.
This is increasingly possible with AI but requires sharing all your health data — everything from your DNA to your medical history to your lifestyle habits.
With FHEML, you could send all this data while keeping it encrypted, and the AI would respond with encrypted health recommendations that you can see alone.
Facial Recognition
From science fiction to the palm of your hand, facial recognition is now a part of our everyday experience. We use facial recognition to enter buildings, unlock our phones, tag people in pictures, and soon, log in to websites everywhere.
However, this requires someone to have your biometric fingerprint, which, in the wrong hands, can be used to impersonate you.
With FHEML, you could authenticate yourself securely without anybody being able to steal your biometric data.
Confidential Smart Contracts
By design, blockchains are public, meaning all the user data flowing into web3 applications are visible to the world.
With FHEML, we can enable private smart contracts where the inputs and outputs are encrypted end-to-end. This means you can safely build decentralized applications that use sensitive personal data – think about on-chain identity, private NFT metadata, or geolocated Dapps.
Advantages of FHEML
Here are the key advantages of FHEML:
- Data Privacy: FHEML enables computations on encrypted data throughout the process.
- Secure Outsourcing: Companies can safely outsource encrypted data for analysis or processing on third-party servers without revealing the underlying information. This is like delegating tasks without the risk of data breaches.
- Reduced Data Breach Risk: Since data remains encrypted even during computations, FHEML mitigates the risk of data breaches or unauthorized access.
- Compliance with Security Standards: Using FHEML allows organizations to comply with stringent international data protection standards (GDPR, HIPAA, etc.), as data is never exposed, even during computations.
- Anonymization and Privacy in Data Analysis: FHEML enables large-scale data analysis while maintaining the complete anonymity of the participants.
- Compatibility with Traditional Machine Learning: Many existing libraries and tools, such as PyTorch or sci-kit-learn, can be adapted to work with FHE, making adopting this technology easier and lowering barriers to entry.
FHEML provides an effective solution for tasks that require complex computations and maximum data protection. With FHEML, organizations can train machine learning models on sensitive data without the risk of information leakage, a revolution in data protection.
Conclusion
FHEML isn’t magic, but it’s pretty darn cool. We’re seeing tons of new projects about FHEML pop up.
For example, many young folks are making an app that can safely check if online users are human. Others are working on a safer version of Face ID using FHEML.
FHEML will be a big deal, and big tech companies will dig it.
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