Improving the efficiency of homomorphic evaluation

Oct 13, 2025 by wbrum.org 120 views

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Introduction

Additive homomorphism is a fundamental concept in cryptography that enables the evaluation of functions on encrypted data without decrypting it first. This property is crucial for various applications, including secure multi-party computation and homomorphic encryption. In this article, we will delve into the world of additive homomorphism and explore schemes that satisfy this property, with a focus on nested encryption.

Additive Homomorphism

Additive homomorphism is a mathematical property that states that the result of combining two encrypted values using a specific operation (e.g., addition) is equivalent to encrypting the sum of the original values. This property is essential for homomorphic encryption, which allows computations to be performed directly on ciphertexts without decrypting them first.

Definition

Given two encrypted values $c_1$ and $c_2$ , where $c_1 = Enc_{pk}(m_1)$ and $c_2 = Enc_{pk}(m_2)$ , additive homomorphism is satisfied if:

Enc_{pk}(m_1 + m_2) = Enc_{pk}(m_1) + Enc_{pk}(m_2)

Implications

Additive homomorphism has significant implications for cryptography and secure computation. It enables the evaluation of functions on encrypted data without decrypting it first, which is essential for various applications, including:

Secure multi-party computation: Additive homomorphism allows multiple parties to perform computations on encrypted data without revealing their inputs.
Homomorphic encryption: Additive homomorphism enables the evaluation of functions on encrypted data without decrypting it first, which is essential for homomorphic encryption.

Schemes for Additive Homomorphism

Several schemes have been proposed to achieve additive homomorphism, including:

1. Brakerski-Gentry-Vaikuntanathan (BGV) Scheme

The BGV scheme is a lattice-based homomorphic encryption scheme that satisfies additive homomorphism. It uses a combination of lattice-based cryptography and ring learning with errors (RLWE) to achieve homomorphic encryption.

Key Generation

The key generation algorithm in the BGV scheme involves generating a public key $pk$ and a secret key $sk$ .

Encryption

The encryption algorithm in the BGV scheme takes a message $m$ and a public key $pk$ as input and produces a ciphertext $c$ .

Homomorphic Evaluation

The BGV scheme supports homomorphic evaluation of functions on encrypted data. Given two ciphertexts $c_1$ and $c_2$ , where $c_1 = Enc_{pk}(m_1)$ and $c_2 = Enc_{pk}(m_2)$ , the BGV scheme allows the evaluation of functions on these ciphertexts without decrypting them first.

2. Brakerski-Gentry-Vaikuntanathan (BGV) Scheme with Double Encryption

The BGV scheme with double encryption is an extension of the BGV scheme that allows for double encryption. This scheme satisfies the following property:

m = Dec_{sk_2}(Dec_{sk_1}(Enc_{pk_2}(Enc_{pk_1}(m))))

Enc_{pk}(m_1) + Enc_{pk}(m_2) = ...

Key Generation

The key generation algorithm in the BGV scheme with double encryption involves generating two public keys $pk_1$ and $pk_2$ and two secret keys $sk_1$ and $sk_2$ .

Encryption

The encryption algorithm in the BGV scheme with double encryption takes a message $m$ and a public key $pk$ as input and produces a ciphertext $c$ .

Homomorphic Evaluation

The BGV scheme with double encryption supports homomorphic evaluation of functions on encrypted data. Given two ciphertexts $c_1$ and $c_2$ , where $c_1 = Enc_{pk_2}(Enc_{pk_1}(m_1))$ and $c_2 = Enc_{pk_2}(Enc_{pk_1}(m_2))$ , the BGV scheme with double encryption allows the evaluation of functions on these ciphertexts without decrypting them first.

Elliptic Curve Cryptography (ECC)

Elliptic curve cryptography (ECC) is a type of public-key cryptography that uses the mathematical structure of elliptic curves to achieve secure key exchange and encryption. ECC has several advantages over traditional public-key cryptography, including:

Key size: ECC keys are typically smaller than traditional public-key cryptography keys, making them more efficient for use in constrained environments.
Computational efficiency: ECC operations are generally faster than traditional public-key cryptography operations, making them more suitable for high-performance applications.

Diffie-Hellman Key Exchange

The Diffie-Hellman key exchange is a fundamental protocol in cryptography that enables two parties to establish a shared secret key over an insecure channel. The Diffie-Hellman key exchange is based on the mathematical structure of elliptic curves and is widely used in various cryptographic applications.

Key Exchange

The Diffie-Hellman key exchange involves two parties, Alice and Bob, who want to establish a shared secret key over an insecure channel. The key exchange protocol involves the following steps:

Key generation: Alice and Bob generate a public key $pk$ and a secret key $sk$ .
Key exchange: Alice and Bob exchange their public keys over the insecure channel.
Key computation: Alice and Bob compute the shared secret key $k$ using their public keys and secret keys.

Elliptic Curve Diffie-Hellman (ECDH)

The elliptic curve Diffie-Hellman (ECDH) is an extension of the Diffie-Hellman key exchange that uses elliptic curves to achieve secure key exchange. ECDH has several advantages over traditional Diffie-Hellman key exchange, including:

Key size: ECDH keys are typically smaller than traditional Diffie-Hellman keys, making them more efficient for use in constrained environments.
Computational efficiency: ECDH operations are generally faster than traditional Diffie-Hellman operations, making them more suitable for high-performance applications.

Conclusion

In this article, we explored the concept of additive homomorphism and its implications for cryptography and secure computation. We also discussed several schemes that satisfy additive homomorphism, including the BGV scheme and the BGV scheme with double encryption. Additionally, we introduced elliptic curve cryptography (ECC) and its applications in secure key exchange and encryption. ECC has several advantages over traditional public-key cryptography, including smaller key sizes and faster computational efficiency.

Future Work

Future work in the area of additive homomorphism and nested encryption includes:

Improving the efficiency of homomorphic evaluation: Developing more efficient algorithms for homomorphic evaluation of functions on encrypted data.
Developing new schemes for additive homomorphism: Designing new schemes that satisfy additive homomorphism and have improved security and efficiency properties.
Applying additive homomorphism to real-world applications: Exploring the use of additive homomorphism in real-world applications, such as secure multi-party computation and homomorphic encryption.

References

Brakerski, Z., Gentry, C., & Vaikuntanathan, V. (2014). (Leveled) fully homomorphic encryption without a multilinear map. In Proceedings of the 45th Annual ACM Symposium on Theory of Computing (pp. 470-478).
Gentry, C. (2009). A fully homomorphic encryption scheme. Ph.D. thesis, Stanford University.
Diffie, W., & Hellman, M. E. (1976). New directions in cryptography. IEEE Transactions on Information Theory, 22(6), 644-654.

Code

The following code snippet demonstrates the BGV scheme with double encryption:

import numpy as np
def bgv_scheme_with_double_encryption(m, pk1, pk2, sk1, sk2):
# Encryption
c1 = encrypt(m, pk1)
c2 = encrypt(c1, pk2)
# Homomorphic evaluation
c3 = add(c2, c2)

# Decryption
m3 = decrypt(c3, sk2)
m4 = decrypt(m3, sk1)

return m4

def encrypt(m, pk):
# BGV scheme encryption
c = np.random.randint(0, 2, size=len(m))
c = c * m
return c
def decrypt(c, sk):
# BGV scheme decryption
m = c * sk
return m
def add(c1, c2):
# Homomorphic addition
c3 = c1 + c2
return c3

Note that this code snippet is a simplified example and is not intended for production use.

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Introduction

In our previous article, we explored the concept of additive homomorphism and its implications for cryptography and secure computation. We also discussed several schemes that satisfy additive homomorphism, including the BGV scheme and the BGV scheme with double encryption. In this article, we will answer some frequently asked questions (FAQs) related to additive homomorphism and nested encryption.

Q&A

Q: What is additive homomorphism?

A: Additive homomorphism is a mathematical property that states that the result of combining two encrypted values using a specific operation (e.g., addition) is equivalent to encrypting the sum of the original values.

Q: What are the implications of additive homomorphism?

A: Additive homomorphism has significant implications for cryptography and secure computation. It enables the evaluation of functions on encrypted data without decrypting it first, which is essential for various applications, including secure multi-party computation and homomorphic encryption.

Q: What are some schemes that satisfy additive homomorphism?

A: Several schemes have been proposed to achieve additive homomorphism, including the BGV scheme and the BGV scheme with double encryption.

Q: What is the BGV scheme?

A: The BGV scheme is a lattice-based homomorphic encryption scheme that satisfies additive homomorphism. It uses a combination of lattice-based cryptography and ring learning with errors (RLWE) to achieve homomorphic encryption.

Q: What is the BGV scheme with double encryption?

A: The BGV scheme with double encryption is an extension of the BGV scheme that allows for double encryption. This scheme satisfies the following property:

m = Dec_{sk_2}(Dec_{sk_1}(Enc_{pk_2}(Enc_{pk_1}(m))))

Enc_{pk}(m_1) + Enc_{pk}(m_2) = ...

Q: What is elliptic curve cryptography (ECC)?

A: Elliptic curve cryptography (ECC) is a type of public-key cryptography that uses the mathematical structure of elliptic curves to achieve secure key exchange and encryption. ECC has several advantages over traditional public-key cryptography, including smaller key sizes and faster computational efficiency.

Q: What is the Diffie-Hellman key exchange?

A: The Diffie-Hellman key exchange is a fundamental protocol in cryptography that enables two parties to establish a shared secret key over an insecure channel. The Diffie-Hellman key exchange is based on the mathematical structure of elliptic curves and is widely used in various cryptographic applications.

Q: What is the elliptic curve Diffie-Hellman (ECDH) protocol?

A: The elliptic curve Diffie-Hellman (ECDH) protocol is an extension of the Diffie-Hellman key exchange that uses elliptic curves to achieve secure key exchange. ECDH has several advantages over traditional Diffie-Hellman key exchange, including smaller key sizes and faster computational efficiency.

Q: What are some real-world applications of additive homomorphism?

A: Additive homomorphism has several real-world applications, including:

Secure multi-party computation: Additive homomorphism enables multiple parties to perform computations on encrypted data without revealing their inputs.
Homomorphic encryption: Additive homomorphism enables the evaluation of functions on encrypted data without decrypting it first, which is essential for homomorphic encryption.

Q: What are some challenges associated with additive homomorphism?

A: Additive homomorphism has several challenges associated with it, including:

Computational efficiency: Additive homomorphism requires efficient algorithms for homomorphic evaluation of functions on encrypted data.
Security: Additive homomorphism requires secure key exchange and encryption protocols to ensure the confidentiality and integrity of the encrypted data.

Conclusion

In this article, we answered some frequently asked questions (FAQs) related to additive homomorphism and nested encryption. We discussed several schemes that satisfy additive homomorphism, including the BGV scheme and the BGV scheme with double encryption. We also introduced elliptic curve cryptography (ECC) and its applications in secure key exchange and encryption. ECC has several advantages over traditional public-key cryptography, including smaller key sizes and faster computational efficiency.

Future Work

Future work in the area of additive homomorphism and nested encryption includes:

Improving the efficiency of homomorphic evaluation: Developing more efficient algorithms for homomorphic evaluation of functions on encrypted data.
Developing new schemes for additive homomorphism: Designing new schemes that satisfy additive homomorphism and have improved security and efficiency properties.
Applying additive homomorphism to real-world applications: Exploring the use of additive homomorphism in real-world applications, such as secure multi-party computation and homomorphic encryption.

References

Brakerski, Z., Gentry, C., & Vaikuntanathan, V. (2014). (Leveled) fully homomorphic encryption without a multilinear map. In Proceedings of the 45th Annual ACM Symposium on Theory of Computing (pp. 470-478).
Gentry, C. (2009). A fully homomorphic encryption scheme. Ph.D. thesis, Stanford University.
Diffie, W., & Hellman, M. E. (1976). New directions in cryptography. IEEE Transactions on Information Theory, 22(6), 644-654.

Code

The following code snippet demonstrates the BGV scheme with double encryption:

import numpy as np
def bgv_scheme_with_double_encryption(m, pk1, pk2, sk1, sk2):
# Encryption
c1 = encrypt(m, pk1)
c2 = encrypt(c1, pk2)
# Homomorphic evaluation
c3 = add(c2, c2)

# Decryption
m3 = decrypt(c3, sk2)
m4 = decrypt(m3, sk1)

return m4

def encrypt(m, pk):
# BGV scheme encryption
c = np.random.randint(0, 2, size=len(m))
c = c * m
return c
def decrypt(c, sk):
# BGV scheme decryption
m = c * sk
return m
def add(c1, c2):
# Homomorphic addition
c3 = c1 + c2
return c3

Note that this code snippet is a simplified example and is not intended for production use.