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SkillsMP は mukul975/Privacy-Data-Protection-Skills から 289 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
mukul975/Privacy-Data-Protection-Skills収集済み skill 289 件中 40 件を表示しています。
Guides audit findings remediation program management including finding prioritization by severity (critical, high, medium, low), owner assignment, remediation planning, deadline tracking, verification testing, closure criteria, escalation protocols, and…
原文の言語: 英語
Guides continuous privacy compliance monitoring implementation including automated control testing, evidence collection automation, real-time compliance dashboards, alert-based remediation workflows, regulatory change integration, and deviation management.…
原文の言語: 英語
Guides preparation for supervisory authority (DPA) inspections and investigations including document readiness checklists, interview preparation for key personnel, technical demonstration procedures, on-site logistics, response protocols, and post-inspection…
原文の言語: 英語
Guides EU Code of Conduct adherence under GDPR Articles 40-41 including EDPB approval requirements, monitoring body accreditation, code drafting, adherence declaration, compliance verification, and complaint handling. Covers sector-specific codes,…
原文の言語: 英語
Guides GDPR certification mechanism implementation per Articles 42-43 including accredited certification body selection, certification criteria per EDPB guidelines, certification scope, periodic audit requirements, seal and mark usage rules, and relationship…
原文の言語: 英語
Guides internal privacy audit program design and execution including risk-based audit planning, scope definition, fieldwork procedures, finding classification, evidence gathering, remediation tracking, and management reporting. Covers audit universe…
原文の言語: 英語
Guides ISO 27701 Privacy Information Management System implementation extending ISO 27001/27002. Covers Clause 5 PIMS-specific requirements, Clause 6 PIMS guidance for ISO 27002, Clause 7 PII controller guidance (Annex A), Clause 8 PII processor guidance…
原文の言語: 英語
Guides privacy program maturity assessment using the AICPA/CIPT Privacy Maturity Model with five levels: Ad Hoc, Repeating, Defined, Managed, and Optimized. Covers assessment methodology across ten privacy domains, scoring criteria, gap analysis, maturity…
原文の言語: 英語
Guides privacy program effectiveness measurement including leading and lagging indicators, KPI definition, benchmarking methodology, executive reporting formats, board-level privacy dashboards, and metric-driven program improvement. Covers operational,…
原文の言語: 英語
Guides SOC 2 Type II Privacy Trust Services Criteria preparation and audit execution. Covers AICPA TSP Section 100 Privacy criteria P1-P8 including notice, choice/consent, collection, use/retention/disposal, access, disclosure, security, and quality. Includes…
原文の言語: 英語
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…
原文の言語: 英語
Automated enforcement of GDPR Article 5(1)(e) storage limitation principle. Covers TTL-based deletion, retention policy engines, archival workflows, legal hold exemptions, and lifecycle automation. Includes technical implementation patterns for automated data…
原文の言語: 英語
Technical enforcement of GDPR Article 5(1)(b) purpose limitation principle. Covers purpose-tagged data stores, access control per purpose, Article 6(4) compatibility assessment factors, and system design for preventing purpose creep. Includes purpose binding…
原文の言語: 英語
Complete guide to LINDDUN privacy threat modeling methodology covering seven threat categories: Linking, Identifying, Non-repudiation, Detecting, Data Disclosure, Unawareness, and Non-compliance. Includes DFD-based analysis, threat tree catalogs, mitigation…
原文の言語: 英語
Architecture guide for GDPR-compliant federated learning systems. Covers horizontal and vertical FL, aggregation strategies (FedAvg, FedProx), communication efficiency, secure aggregation, and differential privacy integration. Includes privacy guarantees…
原文の言語: 英語
Design privacy-preserving analytics systems using differential privacy, k-anonymity, l-diversity, and t-closeness. Covers privacy budget allocation with epsilon tracking, references Google DP library, OpenDP, and Apple PPML. Includes Python differential…
原文の言語: 英語
Architecture patterns for GDPR Article 5(1)(c) data minimization and Article 25(1) data protection by design. Covers field-level encryption, data masking, aggregation, pseudonymization per Article 4(5), and anonymization per Recital 26. Includes ENISA…
原文の言語: 英語
Technical implementation of GDPR Article 25(2) data protection by default. Covers strictest privacy settings as default configuration, minimum data collection, limited storage duration, restricted accessibility, and opt-in rather than opt-out patterns.…
原文の言語: 英語
Guide to implementing homomorphic encryption for privacy-preserving computation under GDPR. Covers scheme selection (BFV, BGV, CKKS, TFHE), Microsoft SEAL, IBM HELib, and Google FHE transpiler. Includes performance benchmarks, parameter tuning, and basic HE…
原文の言語: 英語
Implementation guide for secure multi-party computation enabling privacy-preserving analytics across organizations. Covers secret sharing, garbled circuits, reference frameworks MP-SPDZ and CrypTen, practical deployment patterns, and GDPR alignment for joint…
原文の言語: 英語
Preparation guide for ISO 31700 privacy by design for consumer goods certification. Covers the 30 requirements across design, production, and disposal phases. Includes gap assessment methodology, remediation planning, and mapping to GDPR Article 25 data…
原文の言語: 英語
Assessment of pseudonymization techniques and re-identification risk. Covers tokenization, hashing, encryption-based pseudonymization, and hybrid approaches. Includes re-identification risk scoring using the motivated intruder test, quantitative metrics…
原文の言語: 英語
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,…
原文の言語: 英語
Implement the Kantara Initiative consent receipt specification including machine-readable receipt structure, JWT-based verification mechanisms, receipt lifecycle management, and integration patterns for consent management platforms. Supports ISO/IEC 27560…
原文の言語: 英語
Deploy differential privacy in production systems including epsilon selection strategies, noise calibration with Laplace and Gaussian mechanisms, privacy budget tracking, composition theorems, and Python implementation patterns. Covers both central and local…
原文の言語: 英語
Conduct LINDDUN privacy threat modeling across all seven categories: Linking, Identifying, Non-repudiation, Detecting, Data Disclosure, Unawareness, and Non-compliance. Includes DFD-based analysis, threat trees, privacy-specific mitigation strategies, and…
原文の言語: 英語
Implement the NIST Privacy Framework COMMUNICATE function covering CM.AW awareness raising and CM.PO communication policies. Provides transparency mechanisms, stakeholder engagement frameworks, privacy notice templates, and communication workflow guidance.
原文の言語: 英語
Implement the NIST Privacy Framework CONTROL function covering CT.DM data management, CT.DP data processing policies and procedures, and CT.PO disassociated processing. Provides technical control architectures, data management workflows, and de-identification…
原文の言語: 英語
Implement the NIST Privacy Framework GOVERN function covering GV.AT awareness and training, GV.MT monitoring and review, GV.PO policy development, and GV.RR roles and responsibilities. Provides governance structure templates, training programs, and…
原文の言語: 英語
Implement the NIST Privacy Framework IDENTIFY function including ID.BE business environment, ID.DA data actions, ID.IM improvement, and ID.RA risk assessment subcategories. Provides control mapping, gap analysis templates, and implementation workflows for…
原文の言語: 英語
Implement the NIST Privacy Framework PROTECT function covering PR.AC access control, PR.DS data security, and PR.PO protective policies. Provides technical control implementation guidance, encryption standards, access management architectures, and…
原文の言語: 英語
Build automated PII detection and redaction pipelines using spaCy NER, Microsoft Presidio, and AWS Macie integration. Includes confidence scoring, custom entity type definitions, batch processing workflows, and multi-format document scanning for structured…
原文の言語: 英語
Design privacy API patterns including data subject API for DSAR endpoints, consent API for preference management, deletion API with cascading delete orchestration, and audit API for compliance reporting. Provides OpenAPI specifications, error handling, rate…
原文の言語: 英語
Build privacy-preserving data sharing platforms using synthetic data generation with the SDV library, data clean rooms, secure enclaves, and utility measurement. Covers end-to-end architecture for sharing analytical datasets while preserving individual…
原文の言語: 英語
Build privacy KPI dashboards tracking DSAR volume and response time, breach count and severity, DPIA completion rate, training coverage, and consent rates. Includes metric definitions, data collection patterns, visualization designs, and executive reporting…
原文の言語: 英語
Implement privacy-preserving record linkage across datasets using Bloom filter encoding, secure hash matching, threshold tuning for precision and recall, and false positive management. Enables entity resolution without exposing raw personally identifiable…
原文の言語: 英語
Design and implement Purpose-Based Access Control (PBAC) architecture including purpose ontology definition, policy engine configuration, audit logging of purpose verification at query time, and integration with existing IAM systems. Enforces GDPR Article…
原文の言語: 英語
Guides the combined DPIA and AI Act conformity assessment for AI systems processing personal data. Covers EDPB-EDPS Joint Opinion 5/2021, training data lawfulness under Art. 6 and Art. 9, Art. 22 automated decision-making, algorithmic bias detection, and NIST…
原文の言語: 英語
Guides DPIA for biometric processing systems including facial recognition, fingerprint, voice, iris, and gait analysis. Covers Art. 9 special category requirements, Art. 35(3)(b) mandatory DPIA triggers for large-scale biometric processing, and EDPB…
原文の言語: 英語
Guides DPIA for migrating personal data to cloud infrastructure covering controller-processor analysis under Art. 28, international transfer assessment, encryption requirements, and shared responsibility model evaluation. Activate for cloud adoption, SaaS…
原文の言語: 英語