Implementing homomorphic encryption

Implementing Homomorphic Encryption

Overview

Homomorphic encryption (HE) enables computation on encrypted data without decryption. The data owner encrypts their data, sends the ciphertext to a computing party (e.g., a cloud processor under Article 28), and the computing party performs operations on the ciphertext. The encrypted results are returned to the data owner, who decrypts them to obtain the plaintext result. At no point does the computing party see unencrypted data.

This directly supports GDPR Article 32(1)(a), which identifies encryption as an appropriate technical measure, and Article 25(1), which requires data protection by design. HE enables a controller to outsource computation to a processor while maintaining cryptographic confidentiality of personal data.

HE Scheme Selection

Scheme Comparison

Scheme	Arithmetic Type	Exact/Approximate	Operations	Best For	Libraries
BFV	Integer (modular)	Exact	Add, Multiply (limited depth)	Counting, matching, exact integer computation	Microsoft SEAL, OpenFHE
BGV	Integer (modular)	Exact	Add, Multiply (with modulus switching)	Deep circuits on integers, batched operations	HELib, OpenFHE
CKKS	Real/Complex	Approximate	Add, Multiply, Rotation	ML inference, statistical analysis, floating-point computation	Microsoft SEAL, OpenFHE, HEAAN
TFHE	Boolean/Integer	Exact	Arbitrary (gate-by-gate)	Programmable bootstrapping, arbitrary functions	TFHE-rs, Concrete (Zama)

Decision Criteria

Criterion	BFV/BGV	CKKS	TFHE
Need exact results?	Yes	No (approximate)	Yes
Data type	Integers	Real numbers	Boolean/Small integers
Multiplicative depth	Limited (plan ahead)	Limited (plan ahead)	Unlimited (with bootstrapping)
SIMD batching	Yes (thousands of slots)	Yes (thousands of slots)	Limited
Performance	Fast for shallow circuits	Fast for ML workloads	Slow per operation, fast bootstrapping
Typical latency	Milliseconds (shallow) to seconds	Milliseconds to seconds	Seconds to minutes

Parameter Selection Guide

For BFV/CKKS with Microsoft SEAL, the key parameters are:

Parameter	Description	Security Impact	Performance Impact
poly_modulus_degree (N)	Degree of the polynomial ring	Higher N = more security	Higher N = slower operations, more memory
coeff_modulus	Product of primes defining the coefficient modulus	Determines noise budget and multiplicative depth	More primes = deeper circuits but larger ciphertexts
plain_modulus (BFV only)	Modulus for plaintext encoding	Must accommodate plaintext values	Larger = less noise budget available
scale (CKKS only)	Encoding scale factor	Determines precision of approximate results	Higher scale = more precision but faster noise growth

Recommended parameter sets (128-bit security):

Use Case	N	coeff_modulus bits	Mult Depth	Memory/Ciphertext
Simple addition/comparison	4096	[40, 40]	1	~64 KB
Moderate computation (sum + multiply)	8192	[60, 40, 40, 60]	2-3	~256 KB
ML inference (logistic regression)	16384	[60, 40, 40, 40, 40, 60]	4-5	~1 MB
Deep neural network inference	32768	[60, 40×8, 60]	8+	~4 MB

Architecture for GDPR-Compliant HE Processing

┌────────────────────────────────────────────────────────────┐
│              Data Controller (Prism Data Systems AG)        │
│  ┌──────────────────┐  ┌──────────────────────────────┐   │
│  │ Key Generator     │  │ Encrypt personal data        │   │
│  │ (sk stays here)   │  │ with public key              │   │
│  └────────┬─────────┘  └──────────┬───────────────────┘   │
│           │ pk, rlk, gk           │ ciphertext             │
└───────────┼───────────────────────┼────────────────────────┘
            │                       │
            ▼                       ▼
┌────────────────────────────────────────────────────────────┐
│              Data Processor (Cloud Service)                  │
│  ┌──────────────────────────────────────────────────────┐  │
│  │ Compute on ciphertext using pk, rlk, gk              │  │
│  │ (NEVER has access to sk — cannot decrypt)             │  │
│  │                                                        │  │
│  │ Operations: HE.Add, HE.Multiply, HE.Rotate           │  │
│  └──────────────────────────┬───────────────────────────┘  │
│                              │ encrypted result             │
└──────────────────────────────┼─────────────────────────────┘
                               │
                               ▼
┌────────────────────────────────────────────────────────────┐
│              Data Controller (Prism Data Systems AG)        │
│  ┌──────────────────────────────────────────────────────┐  │
│  │ Decrypt result using sk                               │  │
│  │ Plaintext result = f(data) computed without exposure   │  │
│  └──────────────────────────────────────────────────────┘  │
└────────────────────────────────────────────────────────────┘

Key:
  sk = Secret key (never leaves controller)
  pk = Public key (for encryption and computation)
  rlk = Relinearization keys (for ciphertext multiplication)
  gk = Galois keys (for ciphertext rotation/SIMD operations)

Performance Considerations

Benchmark Reference (Microsoft SEAL, BFV, N=8192, 128-bit security)

Operation	Latency	Throughput (ops/sec)
Key generation	50 ms	20
Encryption	2 ms	500
Decryption	1 ms	1,000
Ciphertext addition	0.1 ms	10,000
Ciphertext multiplication	5 ms	200
Relinearization	3 ms	333
SIMD rotation	4 ms	250

Optimization Strategies

Strategy	Benefit	Trade-off
SIMD batching	Process thousands of values in parallel	Requires data to be structured as vectors
Circuit depth minimization	Fewer multiplications = less noise, smaller parameters	May require algorithmic redesign
Lazy relinearization	Skip intermediate relinearizations	Larger intermediate ciphertexts
Level-aware computation	Use modulus switching to control noise	Requires careful depth planning
Hybrid HE + plaintext	Perform non-sensitive operations in plaintext	Reduces privacy guarantee scope

Implementation Workflow

1. Identify computation — Define the exact mathematical function to compute on encrypted data. Express it as additions and multiplications (and rotations for SIMD).

2. Select scheme — Choose BFV for exact integer results, CKKS for approximate real-number results, TFHE for arbitrary boolean/integer functions.

3. Estimate circuit depth — Count the maximum number of sequential multiplications in the computation graph. This determines parameter requirements.

4. Select parameters — Choose poly_modulus_degree and coeff_modulus to support the required depth at 128-bit security.

5. Implement and test — Implement the computation using an HE library. Test against plaintext reference implementation to verify correctness.

6. Benchmark — Measure encryption, computation, and decryption latency. Verify memory requirements are within infrastructure limits.

7. Deploy — Establish key management (secret key in HSM at controller, public keys distributed to processor), monitoring, and audit logging.

Key Regulatory References

• GDPR Article 25(1) — Data protection by design (encryption as a design measure)
• GDPR Article 32(1)(a) — Encryption as a security measure
• GDPR Article 28 — Processor obligations (HE enables processing without plaintext exposure)
• GDPR Recital 83 — Encryption to maintain security
• ENISA Report: Data Protection Engineering (2022)
• HomomorphicEncryption.org Standard: Homomorphic Encryption Standardization (2018)

Summary

Homomorphic Encryption enables computations on encrypted data without exposing sensitive information, providing a powerful tool for privacy-preserving data processing. By carefully selecting schemes, parameters, and optimization techniques, it is possible to balance security, accuracy, and performance for practical applications. While challenges such as computational overhead and parameter tuning remain, ongoing research and standardization efforts continue to improve usability and efficiency.

This skill equips you with the foundational knowledge to design, implement, and deploy homomorphic encryption solutions aligned with regulatory requirements and real-world constraints. Mastery of these concepts opens pathways to secure data analytics, confidential machine learning, and compliant cloud computing.

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