Fully homomorphic encryption technology allows computers to process encrypted data. This means organisations can run processes on encrypted data.
This can be used by hospitals, who could send the encrypted data to a third-party cloud provider allowing calculations to be made on the encrypted patient data without the cloud service provider seeing what the data contains. When they are finished, they can decrypt the results.
With the increase of cloud computing, using artificial intelligence and processing data remotely, businesses want to ensure their information is secure. Using this type of encryption could help improve the privacy of the data.
What is Fully Homomorphic Encryption?
Fully homomorphic encryption (FHE) is a type of public-key encryption that allows us to perform calculations on encrypted data. Traditional encryption would typically have to decrypt the data to perform calculations. However, with FHE, it allows for some calculations to be carried out on the encrypted data.
The important part is that the result will be the same as if you had done the calculation on the original data.
To put it in simple terms, imagine you have a maths problem in a locked box. You can give the box to someone, and they can carry out approved calculations without opening the box. They will only be able to see the final answer if they have the correct decrypt key.
This makes it very useful if you need to process data but don’t want the processing system to see the original data.
How does Fully Homomorphic Encryption Work?
There are a few key steps:
Encrypting Original Data
The user will use an encryption algorithm and appropriate public key to encrypt the data. This will convert the original data into ciphertext.
Perform Calculations on Encrypted Data
The processing system will receive the encrypted data. It will then perform calculations. Depending on the encryption scheme, these calculations can include adding, multiplying, and more.
Return Encrypted Results
The system will return an encrypted result.
Decrypt the Final Answer
Finally, an authorised user will use the correct secret key to decrypt the result. This could be used by a financial organisation to add up several encrypted customer balances.
Types of Homomorphic Encryption
There are generally three types of homomorphic encryption:
Partially Homomorphic Encryption
This allows one type of mathematical calculation to be carried out unlimited times. This could be adding or multiplying. This can be useful for specific purposes.
Somewhat Homomorphic Encryption
This allows multiple calculations to be carried out; however, there is a limit to how many calculations can be performed. The calculations available are limited by the encryption scheme.
Fully Homomorphic Encryption
This allows unlimited combinations of calculations. For example, adding and multiplying. However, the calculations are limited by the encryption scheme.
Benefits of Fully Homomorphic Encryption
There are several benefits to using this technology:
- Improved Data Privacy – Sensitive data remains encrypted.
- Secure Cloud Computing – Organisations can use third-party computing without exposing their original data.
- Less Exposed Data – The processing system will not have access to the unencrypted data.
- Privacy-Based Analytics – Businesses can perform calculations on protected data.
- Confidential AI – Machine learning systems could process the encrypted data.
This makes it a valuable tool for industries where data privacy is important.
Applications of FHE
There are various potential applications for this:
Healthcare
Hospitals and research organisations have sensitive medical data. Using FHE could allow researchers to perform calculations on encrypted patient data. This would reduce the need to expose personal medical data. Researchers could analyse encrypted health data to find patterns.
Banking & Financial Services
Financial institutions will hold sensitive customer data such as account balances. Encrypted calculations could be used to carry out some financial analytics and risk analysis whilst keeping customer data secure.
Cloud Computing
Cloud providers process large amounts of data for businesses. Using FHE, organisations can look into outsourcing calculations whilst keeping their data encrypted. This can be useful if companies are working with sensitive business data.
Artificial Intelligence
AI often needs to process large amounts of data. This could be used to look into privacy-focused machine learning as the models can process the encrypted inputs and produce encrypted outputs.
Drawbacks
Whilst it provides a lot of privacy benefits, there are some limitations:
- High Computational Needs – Encrypted calculations can use a lot of processing power.
- Performance Issues – More complex calculations can take longer due to the extra mathematical calculations needed.
- Complex to Implement – Developers would need specialist cryptographic knowledge to create and maintain secure systems.
- More Storage – There could be more storage needed for encrypted data and calculations.
Researchers and technology companies are looking to improve the performance of these systems.
The Future of Fully Homomorphic Encryption
With increasing data privacy requirements, encrypted computing is becoming of interest to researchers, developers, and businesses. As cryptography, libraries, and hardware improve, it may become easier to implement practical applications. However, it will depend on performance, cost of implementation, security requirements, and individual organisations’ requirements.
Final Thoughts
Fully homomorphic encryption is an exciting advancement in data security. It allows computers to perform complex calculations on encrypted data.
Despite the performance and implementation issues, it is an exciting field of cryptography with potential applications in healthcare, banking, cloud computing, and artificial intelligence. As the technology improves, it could help organisations balance performing useful data processing whilst improving data privacy.