Homomorphic encryption: origins and use in smart contracts
Introduction
Homomorphic encryption is a powerful cryptography technique that allows mathematical operations on encrypted data without needing to decipher them. This unique capacity has opened up new perspectives for data confidentiality and computer systems security. In this article, we will explore the origins of homomorphic encryption, its different forms and its use in intelligent contracts. We will also examine a concrete example of the application of homomorphic encryption in a smart contract to illustrate its operation and its advantages.
Origins of homomorphic encryption
The concept of homomorphic encryption dates back to the pioneering works of Rivest, Adleman and Dertouzos in 1978, which introduced the concept of public key encryption. However, it was not until the work of Craig Gentry in 2009 that homomorphic encryption became a practical reality. Gentry has developed the first fully functional homomorphic encryption system based on complex mathematical constructions such as Euclidian networks and ideal networks.
Homomorphic encryption types
There are different types of homomorphic encryption, including partially homomorphic encryption and completely homomorphic encryption. Partially homomorphic encryption makes it possible to carry out certain mathematical operations on encrypted data, such as addition or multiplication, but not both simultaneously. On the other hand, completely homomorphic encryption makes it possible to carry out both adding and multiplication operations on encrypted data.
Use of homomorphic encryption in smart contracts
Homomorphic encryption offers significant advantages in the field of intelligent contracts. It guarantees data confidentiality while allowing the execution of calculations on this encrypted data. This opens up new possibilities for the confidentiality of sensitive data, such as medical information or financial data, while allowing intelligent contracts to operate on this data in a secure manner.
Concrete example: Smart Salary Calculation Contract
Let's take the example of a smart salary calculation contract based on homomorphic encryption. In this case, sensitive data such as the employee's salary is encrypted before being stored on the blockchain. The smart contract then uses homomorphic operations to perform calculations on this encrypted data.
Suppose the smart contract receives the figures of the employee's monthly salary and the applicable tax rate. The contract uses homomorphic addition and multiplication operations to calculate the amount of tax to be deducted from the quantified salary. The encrypted results are then sent back to the employee, who can decrypt them locally to get the actual amount of their take home pay.
This process guarantees the confidentiality of the employee's wage data while allowing the smart contract to carry out calculations on this data without deciphering them. Thus, even if the smart contract is executed on a public blockchain, the employee's sensitive information remains protected.
Code example of smart contrat without homomorphic Encryption :
```
```
In this contract, we have a public variable `value` of type `uint` (unsigned integer) which stores a value. The `setValue` function allows you to modify this value by taking a new integer as a parameter and storing it in the `value` variable.
Now, let's apply homomorphic encryption to the contract to ensure the confidentiality of the stored value. For this we will use a homomorphic encryption library such as "HomomorphicEncryptionLibrary". Here is the version of the smart contract with homomorphic encryption:
```
```
In this modified version, we have imported the "HomomorphicEncryptionLibrary.sol" library, which contains the necessary homomorphic encryption and decryption functions. The `encryptedValue` variable now stores the value encrypted using homomorphic encryption.
The `setValue` function takes the new encrypted value `encryptedNewValue` as a parameter and checks if the decryption of this value matches the address of the caller. This ensures that only the person with the appropriate decryption key can modify the encrypted value.
The `getValue` function returns the value decrypted using the decryption function of the `HomomorphicEncryptionLibrary` library.
With this modification, the smart contract uses homomorphic encryption to ensure the confidentiality of the stored value while allowing certain operations to be performed on the encrypted value.
Homomorphic encryption offers an innovative solution for data privacy in smart contracts. By allowing calculations to be performed on encrypted data, it opens up new possibilities for the security and confidentiality of sensitive information. Practical applications such as encrypted salary calculations demonstrate the potential of homomorphic encryption in smart contracts.
However, it should be noted that homomorphic encryption has constraints in terms of performance and mathematical complexity. Further research and development is needed to improve performance and make homomorphic encryption more accessible to developers.
In conclusion, homomorphic encryption paves the way for new privacy approaches in smart contracts. Its use can provide an additional layer of security and privacy to blockchain applications, while allowing computations to be performed on sensitive data. With continued advances in this area, homomorphic encryption promises to play a key role in the future of smart contracts and data privacy.
Introduction
Homomorphic encryption is a powerful cryptography technique that allows mathematical operations on encrypted data without needing to decipher them. This unique capacity has opened up new perspectives for data confidentiality and computer systems security. In this article, we will explore the origins of homomorphic encryption, its different forms and its use in intelligent contracts. We will also examine a concrete example of the application of homomorphic encryption in a smart contract to illustrate its operation and its advantages.
Origins of homomorphic encryption
The concept of homomorphic encryption dates back to the pioneering works of Rivest, Adleman and Dertouzos in 1978, which introduced the concept of public key encryption. However, it was not until the work of Craig Gentry in 2009 that homomorphic encryption became a practical reality. Gentry has developed the first fully functional homomorphic encryption system based on complex mathematical constructions such as Euclidian networks and ideal networks.
Homomorphic encryption types
There are different types of homomorphic encryption, including partially homomorphic encryption and completely homomorphic encryption. Partially homomorphic encryption makes it possible to carry out certain mathematical operations on encrypted data, such as addition or multiplication, but not both simultaneously. On the other hand, completely homomorphic encryption makes it possible to carry out both adding and multiplication operations on encrypted data.
Use of homomorphic encryption in smart contracts
Homomorphic encryption offers significant advantages in the field of intelligent contracts. It guarantees data confidentiality while allowing the execution of calculations on this encrypted data. This opens up new possibilities for the confidentiality of sensitive data, such as medical information or financial data, while allowing intelligent contracts to operate on this data in a secure manner.
Concrete example: Smart Salary Calculation Contract
Let's take the example of a smart salary calculation contract based on homomorphic encryption. In this case, sensitive data such as the employee's salary is encrypted before being stored on the blockchain. The smart contract then uses homomorphic operations to perform calculations on this encrypted data.
Suppose the smart contract receives the figures of the employee's monthly salary and the applicable tax rate. The contract uses homomorphic addition and multiplication operations to calculate the amount of tax to be deducted from the quantified salary. The encrypted results are then sent back to the employee, who can decrypt them locally to get the actual amount of their take home pay.
This process guarantees the confidentiality of the employee's wage data while allowing the smart contract to carry out calculations on this data without deciphering them. Thus, even if the smart contract is executed on a public blockchain, the employee's sensitive information remains protected.
Code example of smart contrat without homomorphic Encryption :
```
Код:
solidity
contract SimpleContract {
uint public value;
function setValue(uint newValue) public {
value = newValue;
}
}In this contract, we have a public variable `value` of type `uint` (unsigned integer) which stores a value. The `setValue` function allows you to modify this value by taking a new integer as a parameter and storing it in the `value` variable.
Now, let's apply homomorphic encryption to the contract to ensure the confidentiality of the stored value. For this we will use a homomorphic encryption library such as "HomomorphicEncryptionLibrary". Here is the version of the smart contract with homomorphic encryption:
```
Код:
solidity
import "HomomorphicEncryptionLibrary.sol";
contract EncryptedContract {
using HomomorphicEncryptionLibrary for uint;
bytes encryptedValue;
function setValue(bytes memory encryptedNewValue) public {
require(msg.sender == HomomorphicEncryptionLibrary.decrypt(encryptedNewValue), "Invalid decryption key!");
encryptedValue = encryptedNewValue;
}
function getValue() public view returns (uint) {
return HomomorphicEncryptionLibrary.decrypt(encryptedValue);
}
}In this modified version, we have imported the "HomomorphicEncryptionLibrary.sol" library, which contains the necessary homomorphic encryption and decryption functions. The `encryptedValue` variable now stores the value encrypted using homomorphic encryption.
The `setValue` function takes the new encrypted value `encryptedNewValue` as a parameter and checks if the decryption of this value matches the address of the caller. This ensures that only the person with the appropriate decryption key can modify the encrypted value.
The `getValue` function returns the value decrypted using the decryption function of the `HomomorphicEncryptionLibrary` library.
With this modification, the smart contract uses homomorphic encryption to ensure the confidentiality of the stored value while allowing certain operations to be performed on the encrypted value.
Homomorphic encryption offers an innovative solution for data privacy in smart contracts. By allowing calculations to be performed on encrypted data, it opens up new possibilities for the security and confidentiality of sensitive information. Practical applications such as encrypted salary calculations demonstrate the potential of homomorphic encryption in smart contracts.
However, it should be noted that homomorphic encryption has constraints in terms of performance and mathematical complexity. Further research and development is needed to improve performance and make homomorphic encryption more accessible to developers.
In conclusion, homomorphic encryption paves the way for new privacy approaches in smart contracts. Its use can provide an additional layer of security and privacy to blockchain applications, while allowing computations to be performed on sensitive data. With continued advances in this area, homomorphic encryption promises to play a key role in the future of smart contracts and data privacy.
