mukul975/privacy-data-protection-skills

implementing-homomorphic-encryption

- Guide to implementing homomorphic encryption for privacy-preserving computation under GDPR.

소스 보기

사용 방법

  1. 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
  2. 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
  3. 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/mukul975/privacy-data-protection-skills --skill implementing-homomorphic-encryption

시작 전 확인

  • 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
  • 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서

원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.

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

SchemeArithmetic TypeExact/ApproximateOperationsBest ForLibraries
BFVInteger (modular)ExactAdd, Multiply (limited depth)Counting, matching, exact integer computationMicrosoft SEAL, OpenFHE
BGVInteger (modular)ExactAdd, Multiply (with modulus switching)Deep circuits on integers, batched operationsHELib, OpenFHE
CKKSReal/ComplexApproximateAdd, Multiply, RotationML inference, statistical analysis, floating-point computationMicrosoft SEAL, OpenFHE, HEAAN
TFHEBoolean/IntegerExactArbitrary (gate-by-gate)Programmable bootstrapping, arbitrary functionsTFHE-rs, Concrete (Zama)

Decision Criteria

CriterionBFV/BGVCKKSTFHE
Need exact results?YesNo (approximate)Yes
Data typeIntegersReal numbersBoolean/Small integers
Multiplicative depthLimited (plan ahead)Limited (plan ahead)Unlimited (with bootstrapping)
SIMD batchingYes (thousands of slots)Yes (thousands of slots)Limited
PerformanceFast for shallow circuitsFast for ML workloadsSlow per operation, fast bootstrapping
Typical latencyMilliseconds (shallow) to secondsMilliseconds to secondsSeconds to minutes

Parameter Selection Guide

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

ParameterDescriptionSecurity ImpactPerformance Impact
polymodulusdegree (N)Degree of the polynomial ringHigher N = more securityHigher N = slower operations, more memory
coeff_modulusProduct of primes defining the coefficient modulusDetermines noise budget and multiplicative depthMore primes = deeper circuits but larger ciphertexts
plain_modulus (BFV only)Modulus for plaintext encodingMust accommodate plaintext valuesLarger = less noise budget available
scale (CKKS only)Encoding scale factorDetermines precision of approximate resultsHigher scale = more precision but faster noise growth

Recommended parameter sets (128-bit security):

Use CaseNcoeff_modulus bitsMult DepthMemory/Ciphertext
Simple addition/comparison4096[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 inference32768[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)

OperationLatencyThroughput (ops/sec)
Key generation50 ms20
Encryption2 ms500
Decryption1 ms1,000
Ciphertext addition0.1 ms10,000
Ciphertext multiplication5 ms200
Relinearization3 ms333
SIMD rotation4 ms250

Optimization Strategies

StrategyBenefitTrade-off
SIMD batchingProcess thousands of values in parallelRequires data to be structured as vectors
Circuit depth minimizationFewer multiplications = less noise, smaller parametersMay require algorithmic redesign
Lazy relinearizationSkip intermediate relinearizationsLarger intermediate ciphertexts
Level-aware computationUse modulus switching to control noiseRequires careful depth planning
Hybrid HE + plaintextPerform non-sensitive operations in plaintextReduces 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).
  1. Select scheme — Choose BFV for exact integer results, CKKS for approximate real-number results, TFHE for arbitrary boolean/integer functions.
  1. Estimate circuit depth — Count the maximum number of sequential multiplications in the computation graph. This determines parameter requirements.
  1. Select parameters — Choose polymodulusdegree and coeff_modulus to support the required depth at 128-bit security.
  1. Implement and test — Implement the computation using an HE library. Test against plaintext reference implementation to verify correctness.
  1. Benchmark — Measure encryption, computation, and decryption latency. Verify memory requirements are within infrastructure limits.
  1. 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)

관련 작업

같은 저장소의 Skills

더 많은 Skills

모든 Skills
mukul975
커뮤니티

applying-privacy-design-patterns

- Systematic application of the eight privacy design patterns per Hoepman: minimize, hide, separate, abstract, inform, control, enforce, and demonstrate. Covers pattern selection methodology per processing activity, mapping to GDPR principles, and practical implementation guidance for privacy-by-design system architecture.

설치 수
1
GitHub Stars
315
업데이트
3월 16일
mukul975
커뮤니티

brazil-lgpd

- Guides compliance with Brazil's Lei Geral de Proteção de Dados (LGPD, Lei 13.709/2018). Covers the 10 lawful bases under Art. 7, DPO appointment, ANPD enforcement, data subject rights under Arts. 17-22, and international transfer mechanisms. Keywords: LGPD, Brazil data protection, ANPD, lawful bases, data subject rights, international transfers.

설치 수
1
GitHub Stars
315
업데이트
3월 16일
mukul975
커뮤니티

adequacy-assessment

- Guides assessment of third-country adequacy decisions under GDPR Article 45 for international data transfers. Covers the current EC adequacy decisions list, adequacy assessment criteria, partial adequacy handling, and monitoring of adequacy decision reviews. Keywords: adequacy decision, Article 45, third country, adequate protection, EC adequacy list.

설치 수
1
GitHub Stars
295
업데이트
3월 16일
mukul975
커뮤니티

age-gating-services

- Implements age-gating mechanisms for online services to restrict access based on user age. Covers hard gates versus soft gates, neutral age prompts, re-verification triggers, circumvention prevention, and regulatory requirements under GDPR, COPPA, UK Online Safety Act, and DSA. Keywords: age gate, age restriction, neutral prompt, children, online services, access control.

설치 수
1
GitHub Stars
295
업데이트
3월 16일