Data Protection And Encryption
cbrock84/headcount
Protects data itself rather than the systems around it — classifying what you hold, encrypting in transit and at rest and understanding what each actually defends against, managing keys and their…
Agent skill
Selecting Privacy Enhancing Technologies
Comprehensive PET selection guide covering differential privacy, homomorphic encryption, secure multi-party computation, federated learning, zero-knowledge proofs, and trusted execution environments.
Install Selecting Privacy Enhancing Technologies
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill selecting-privacy-enhancing-technologies -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills selecting-privacy-enhancing-technologies --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/mukul975/Privacy-Data-Protection-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/privacy/selecting-privacy-enhancing-technologies .claude/skills/selecting-privacy-enhancing-technologies && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "selecting-privacy-enhancing-technologies" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/selecting-privacy-enhancing-technologies into .claude/skills/selecting-privacy-enhancing-technologies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "selecting-privacy-enhancing-technologies", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/selecting-privacy-enhancing-technologiesType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill selecting-privacy-enhancing-technologies -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills selecting-privacy-enhancing-technologies --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Privacy-Data-Protection-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/privacy/selecting-privacy-enhancing-technologies .agents/skills/selecting-privacy-enhancing-technologies && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "selecting-privacy-enhancing-technologies" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/selecting-privacy-enhancing-technologies into .agents/skills/selecting-privacy-enhancing-technologies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "selecting-privacy-enhancing-technologies", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill selecting-privacy-enhancing-technologies -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills selecting-privacy-enhancing-technologies --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Privacy-Data-Protection-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/privacy/selecting-privacy-enhancing-technologies .cursor/skills/selecting-privacy-enhancing-technologies && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "selecting-privacy-enhancing-technologies" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/selecting-privacy-enhancing-technologies into .cursor/skills/selecting-privacy-enhancing-technologies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "selecting-privacy-enhancing-technologies", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/mukul975/Privacy-Data-Protection-Skills.git --path skills/privacy/selecting-privacy-enhancing-technologies--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill selecting-privacy-enhancing-technologies -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills selecting-privacy-enhancing-technologies --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Privacy-Data-Protection-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/privacy/selecting-privacy-enhancing-technologies .gemini/skills/selecting-privacy-enhancing-technologies && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "selecting-privacy-enhancing-technologies" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/selecting-privacy-enhancing-technologies into .gemini/skills/selecting-privacy-enhancing-technologies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "selecting-privacy-enhancing-technologies", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install mukul975/Privacy-Data-Protection-Skills selecting-privacy-enhancing-technologiesInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill selecting-privacy-enhancing-technologies -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mukul975/Privacy-Data-Protection-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/privacy/selecting-privacy-enhancing-technologies .github/skills/selecting-privacy-enhancing-technologies && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "selecting-privacy-enhancing-technologies" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/selecting-privacy-enhancing-technologies into .github/skills/selecting-privacy-enhancing-technologies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "selecting-privacy-enhancing-technologies", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill selecting-privacy-enhancing-technologies -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills selecting-privacy-enhancing-technologies --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Privacy-Data-Protection-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/privacy/selecting-privacy-enhancing-technologies .opencode/skills/selecting-privacy-enhancing-technologies && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "selecting-privacy-enhancing-technologies" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/selecting-privacy-enhancing-technologies into .opencode/skills/selecting-privacy-enhancing-technologies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "selecting-privacy-enhancing-technologies", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
selecting-privacy-enhancing-technologies- GitHub stars
- 297
- Token cost
- ~2.6k tokens
- SKILL.md length
- 1,174 words
- Files
- 5 (incl. scripts, references, assets)
- Skills in repo
- 280
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Comprehensive PET selection guide covering differential privacy, homomorphic encryption, secure multi-party computation, federated learning, zero-knowledge proofs, and trusted execution environments.
- Works in 10 steps: Differential Privacy (DP) → Homomorphic Encryption (HE) → Secure Multi-Party Computation (SMPC) → …
- Tasks that involve Privacy and GDPR
- SKILL.md covers Overview, PET Taxonomy, Use-Case Matching Matrix and PET Selection Decision Framework, plus 1 more section
- Runs Python scripts from its folder
What it does
Selecting Privacy Enhancing Technologies is an agent skill from mukul975/Privacy-Data-Protection-Skills. Comprehensive PET selection guide covering differential privacy, homomorphic encryption, secure multi-party computation, federated learning, zero-knowledge proofs, and trusted execution environments. Includes use-case matching matrix, performance comparison, and GDPR alignment assessment for each technology.
Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts, reference files and assets (for example `assets/template.md`, `references/standards.md` and `references/workflows.md`).
It sits in Legal & Compliance, covering Privacy and GDPR and Cryptography. The repository describes itself as: 282+ structured privacy & data protection skills for AI agents. GDPR, CCPA, EU AI Act, HIPAA, LGPD, PIPL, DPDP Act. The licence is Apache-2.0.
When your agent uses it
- Tasks that involve Privacy and GDPR
- Tasks that involve Cryptography
Example prompts
- “/selecting-privacy-enhancing-technologies”
Requirements
- Python 3
Workflow steps
10 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 9b2ef9e. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Selecting Privacy Enhancing Technologies loads about 2.6k tokens when it runs, and up to ~5k if it reads all its reference files. Until then it costs about 88 tokens; SKILL.md has 1,174 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from mukul975/Privacy-Data-Protection-Skills at commit 9b2ef9e, republished under its Apache-2.0 licence (© mukul975). 1,174 words, ~2,623 tokens.
Download SKILL.mdSave it as
.claude/skills/selecting-privacy-enhancing-technologies/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- selecting-privacy-enhancing-technologies
- description
- Comprehensive PET selection guide covering differential privacy, homomorphic encryption, secure multi-party computation, federated learning, zero-knowledge proofs, and trusted execution environments. Includes use-case matching matrix, performance comparison, and GDPR alignment assessment for each technology.
- license
- Apache-2.0
- metadata.author
- mukul975
- metadata.version
- 1.0
- metadata.domain
- privacy
- metadata.subdomain
- privacy-by-design
- metadata.tags
- pet-selection, homomorphic-encryption, federated-learning, zero-knowledge-proofs, secure-computation
Selecting Privacy-Enhancing Technologies
Overview
Privacy-Enhancing Technologies (PETs) are technical measures that protect personal data during collection, processing, storage, and sharing. The GDPR does not prescribe specific technologies but requires "appropriate technical measures" (Article 25(1), Article 32(1)) to implement data protection principles. The European Commission's February 2025 communication on PETs and the ENISA 2023 report on engineering privacy by design with PETs provide regulatory context for PET adoption.
Selecting the right PET depends on the processing scenario, data sensitivity, computational requirements, and the specific privacy property needed (confidentiality, anonymity, unlinkability, or transparency).
PET Taxonomy
1. Differential Privacy (DP)
Privacy property: Statistical indistinguishability — the output of an analysis is approximately the same whether or not any individual's data is included.
| Characteristic | Detail |
|---|---|
| Approach | Add calibrated noise to query results or model gradients |
| Privacy guarantee | Mathematically provable (epsilon, delta) bounds |
| Data utility | Configurable via epsilon; lower epsilon = more privacy, less utility |
| Performance overhead | Minimal for query-time noise; moderate for DP-SGD training |
| Maturity | Production-ready (Apple, Google, US Census Bureau) |
| GDPR relevance | Supports Recital 26 anonymization when epsilon is sufficiently small; Art. 25(1) by design measure |
Best for: Statistical analytics, aggregate reporting, ML model training on sensitive data.
Libraries: Google DP Library, OpenDP, IBM diffprivlib, PyDP.
2. Homomorphic Encryption (HE)
Privacy property: Computation on encrypted data — results are decrypted only by the data owner.
| Characteristic | Detail |
|---|---|
| Approach | Encrypt data such that mathematical operations on ciphertext produce valid results when decrypted |
| Privacy guarantee | Cryptographic (lattice-based hardness assumptions) |
| Data utility | Exact results (no noise added) |
| Performance overhead | High: 1,000x-1,000,000x slower than plaintext, depending on scheme and operation |
| Maturity | Emerging for production; active research on performance optimization |
| GDPR relevance | Art. 32(1)(a) encryption as security measure; enables processing without exposing plaintext to processor |
Schemes:
- BFV (Brakerski/Fan-Vercauteren): Integer arithmetic, exact. Suitable for counting, matching.
- BGV (Brakerski-Gentry-Vaikuntanathan): Integer arithmetic with modulus switching. Efficient for deep circuits.
- CKKS (Cheon-Kim-Kim-Song): Approximate arithmetic on real/complex numbers. Suitable for ML inference.
- TFHE (Torus FHE): Fast bootstrapping, gate-by-gate evaluation. Suitable for boolean circuits.
Libraries: Microsoft SEAL, IBM HELib, Google FHE (Fully Homomorphic Encryption transpiler), TFHE-rs, OpenFHE.
3. Secure Multi-Party Computation (SMPC)
Privacy property: Joint computation without revealing individual inputs — each party learns only the final result.
| Characteristic | Detail |
|---|---|
| Approach | Secret sharing, garbled circuits, or oblivious transfer protocols |
| Privacy guarantee | Information-theoretic (for secret sharing) or computational (for garbled circuits) |
| Data utility | Exact results |
| Performance overhead | Moderate to high; depends on circuit complexity and number of parties |
| Maturity | Production deployments in financial services, healthcare consortia |
| GDPR relevance | Enables joint controllership (Art. 26) analytics without data sharing; supports data minimization (Art. 5(1)(c)) |
Frameworks: MP-SPDZ, CrypTen (Meta), MOTION, ABY/ABY3, Sharemind.
4. Federated Learning (FL)
Privacy property: Model training without centralizing data — raw data never leaves the data owner's environment.
| Characteristic | Detail |
|---|---|
| Approach | Distribute model training to data owners; aggregate only model updates (gradients) |
| Privacy guarantee | Data locality (raw data stays on device); strengthened with secure aggregation and DP |
| Data utility | Depends on data distribution across participants (IID vs non-IID) |
| Performance overhead | Communication overhead for gradient exchange; multiple training rounds |
| Maturity | Production at Google (Gboard), Apple (Siri), hospitals (NVIDIA FLARE) |
| GDPR relevance | Supports Art. 5(1)(c) minimization; reduces cross-border transfer requirements (Chapter V) |
Frameworks: TensorFlow Federated, PySyft (OpenMined), NVIDIA FLARE, Flower, FedML.
5. Zero-Knowledge Proofs (ZKP)
Privacy property: Prove a statement is true without revealing the underlying data.
| Characteristic | Detail |
|---|---|
| Approach | Prover demonstrates knowledge of a secret (e.g., age ≥ 18) without revealing the secret (date of birth) |
| Privacy guarantee | Soundness (false statements cannot be proven) and zero-knowledge (verifier learns nothing beyond the statement's truth) |
| Data utility | Binary verification (proof valid/invalid); no data disclosed |
| Performance overhead | Proof generation: moderate to high; proof verification: fast |
| Maturity | Production in blockchain identity; emerging in enterprise identity verification |
| GDPR relevance | Ultimate data minimization — prove compliance without disclosing data; supports Art. 5(1)(c), Art. 25 |
Systems: zk-SNARKs (Groth16, PLONK), zk-STARKs, Bulletproofs.
6. Trusted Execution Environments (TEE)
Privacy property: Isolated computation — data is processed inside a hardware-protected enclave that even the system administrator cannot access.
| Characteristic | Detail |
|---|---|
| Approach | Hardware-enforced isolation using secure enclaves (Intel SGX, AMD SEV, ARM TrustZone, AWS Nitro) |
| Privacy guarantee | Hardware-based attestation and memory encryption |
| Data utility | Full computation capability inside enclave |
| Performance overhead | Low to moderate; limited enclave memory may require data streaming |
| Maturity | Production: Azure Confidential Computing, AWS Nitro Enclaves, Google Confidential VMs |
| GDPR relevance | Art. 32(1)(a) encryption in processing; Art. 28 processor guarantees; protects against insider threats |
Show full SKILL.md (451 more words)Show less
Use-Case Matching Matrix
| Use Case | DP | HE | SMPC | FL | ZKP | TEE | Recommended Primary |
|---|---|---|---|---|---|---|---|
| Aggregate analytics on customer data | 5 | 2 | 3 | 2 | 1 | 3 | Differential Privacy |
| ML model training on distributed hospital data | 3 | 1 | 2 | 5 | 1 | 3 | Federated Learning |
| Credit scoring without sharing financial records | 2 | 3 | 5 | 2 | 2 | 3 | Secure MPC |
| Age verification without revealing date of birth | 1 | 1 | 1 | 1 | 5 | 2 | Zero-Knowledge Proofs |
| Processing encrypted customer data in third-party cloud | 2 | 5 | 2 | 1 | 1 | 4 | Homomorphic Encryption |
| Anti-money laundering across banks | 2 | 2 | 5 | 3 | 2 | 3 | Secure MPC |
| Private genomic analysis | 3 | 4 | 3 | 3 | 1 | 4 | HE + TEE combination |
| Confidential inference on cloud-hosted ML models | 2 | 4 | 2 | 1 | 1 | 5 | Trusted Execution Environment |
| Privacy-preserving surveys and polls | 5 | 1 | 3 | 1 | 2 | 1 | Differential Privacy |
| Secure keyword search on encrypted database | 1 | 4 | 1 | 1 | 1 | 4 | Homomorphic Encryption |
Scores: 1 = poor fit, 5 = excellent fit
PET Selection Decision Framework
Step 1: Define Privacy Requirements
- What data protection principle is primary? (minimization, confidentiality, anonymization, unlinkability)
- Is the goal to protect data in transit, at rest, or during processing?
- Is the adversary model honest-but-curious or malicious?
- Are there cross-organizational boundaries (multiple controllers/processors)?
Step 2: Assess Constraints
- What is the acceptable performance overhead? (real-time vs batch)
- What is the computational budget? (HE requires significantly more resources)
- How many parties are involved? (SMPC scales with party count)
- Is there a hardware trust anchor available? (TEE requires specific hardware)
- What is the team's cryptographic engineering expertise?
Step 3: Evaluate Combinations
Many production deployments combine PETs for defense in depth:
| Combination | Benefit |
|---|---|
| FL + DP | Federated learning with differentially private gradient updates prevents gradient inversion attacks |
| FL + Secure Aggregation | Aggregation server never sees individual gradients |
| HE + TEE | Process encrypted data inside an enclave for double protection |
| SMPC + DP | Add noise to SMPC outputs for formal anonymization guarantee |
| ZKP + TEE | Prove computation was performed correctly inside an enclave without revealing inputs |
Step 4: GDPR Alignment Verification
- Does the selected PET satisfy Article 25(1) data protection by design?
- Does it implement a specific Article 32(1) security measure?
- Does it support the data minimization principle under Article 5(1)(c)?
- If anonymization is claimed, does it meet Recital 26 standards against singling out, linkability, and inference?
- Is the PET's privacy guarantee documented sufficiently for DPIA (Article 35)?
Key Regulatory References
- GDPR Article 25(1) — Data protection by design
- GDPR Article 32(1)(a) — Encryption and pseudonymisation as security measures
- GDPR Article 5(1)(c) — Data minimization principle
- GDPR Recital 26 — Anonymous information
- GDPR Recital 78 — Technical measures for data protection
- ENISA Report: Data Protection Engineering (2022)
- European Commission Communication on PETs (February 2025)
- EDPB Guidelines 4/2019 on Article 25 Data Protection by Design and by Default
© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (scripts, references, assets) in skills/privacy/selecting-privacy-enhancing-technologies of mukul975/Privacy-Data-Protection-Skills.
- SKILL.md
- assets/template.md
- references/standards.md
- references/workflows.md
- scripts/process.py
Open the folder on GitHubat commit 9b2ef9e
Compare with similar skills
Selecting Privacy Enhancing Technologies next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Selecting Privacy Enhancing Technologies this skillmukul975/Privacy-Data-Protection-Skills | 297 | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Data Protection And Encryptioncbrock84/headcount | 2k | — | ~1.3k | Automated safety check: Pass | MIT | |
| C15tc15t/c15t | 1.9k | 1 repos | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| HIPAA Safe Harbor Coverage Auditmaziyarpanahi/openmed | 5.5k | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | |
| Korean Privacy Termskimlawtech/korean-privacy-terms | 586 | — | ~2.9k | Automated safety check: Pass | Apache-2.0 | |
| Gdpr ComplianceSushegaad/Claude-Skills-Governance-Risk-and-Compliance | 943 | 1 repos | ~3.9k | Automated safety check: Pass | MIT |
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Categories
Questions about Selecting Privacy Enhancing Technologies
What does Selecting Privacy Enhancing Technologies do?
Comprehensive PET selection guide covering differential privacy, homomorphic encryption, secure multi-party computation, federated learning, zero-knowledge proofs, and trusted execution environments. Selecting Privacy Enhancing Technologies is an agent skill from mukul975/Privacy-Data-Protection-Skills. Comprehensive PET selection guide covering differential privacy, homomorphic encryption, secure multi-party computation, federated learning, zero-knowledge proofs, and trusted execution environments.
When should I use Selecting Privacy Enhancing Technologies?
Selecting Privacy Enhancing Technologies fits situations like: tasks that involve Privacy and GDPR; tasks that involve Cryptography.
How do I install Selecting Privacy Enhancing Technologies in Claude Code?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill selecting-privacy-enhancing-technologies -a claude-code`. Or copy the skill folder (skills/privacy/selecting-privacy-enhancing-technologies in mukul975/Privacy-Data-Protection-Skills) into .claude/skills/selecting-privacy-enhancing-technologies in your project. Claude Code loads it when a task matches its description.
How do I install Selecting Privacy Enhancing Technologies in Codex?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill selecting-privacy-enhancing-technologies -a codex`. Or copy the skill folder (skills/privacy/selecting-privacy-enhancing-technologies in mukul975/Privacy-Data-Protection-Skills) into .agents/skills/selecting-privacy-enhancing-technologies in your project. Codex loads it when a task matches its description.
Can I use Selecting Privacy Enhancing Technologies in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill selecting-privacy-enhancing-technologies -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/selecting-privacy-enhancing-technologies, .gemini/skills/selecting-privacy-enhancing-technologies, .github/skills/selecting-privacy-enhancing-technologies and .opencode/skills/selecting-privacy-enhancing-technologies in your project.
What does Selecting Privacy Enhancing Technologies need to run?
Going by SKILL.md and its folder, Selecting Privacy Enhancing Technologies needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Selecting Privacy Enhancing Technologies access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Selecting Privacy Enhancing Technologies safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Selecting Privacy Enhancing Technologies use?
Selecting Privacy Enhancing Technologies is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Selecting Privacy Enhancing Technologies use?
About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.3k tokens, read only when the agent opens those files.
What are the alternatives to Selecting Privacy Enhancing Technologies?
Skills that share tags, products or a category with Selecting Privacy Enhancing Technologies: Data Protection And Encryption (cbrock84/headcount, 2k stars), C15t (c15t/c15t, 1.9k stars), HIPAA Safe Harbor Coverage Audit (maziyarpanahi/openmed, 5.5k stars) and Korean Privacy Terms (kimlawtech/korean-privacy-terms, 586 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Selecting Privacy Enhancing Technologies?
mukul975 (a GitHub user) maintains it in mukul975/Privacy-Data-Protection-Skills, which has 297 GitHub stars. The repository holds 280 skills in this directory. The repository was last updated on March 16, 2026.
Source: mukul975/Privacy-Data-Protection-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.