References: Ethics, Privacy, and Security
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Information Privacy - Wikipedia - Comprehensive overview of information privacy concepts, legal frameworks (GDPR, CCPA), and the relationship between data collection, consent, and individual rights relevant to employee data analytics.
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Differential Privacy - Wikipedia - Explains the mathematical framework for providing privacy guarantees when publishing aggregate statistics, ensuring individual records cannot be reverse-engineered from query results.
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Role-Based Access Control - Wikipedia - Covers RBAC models for restricting system access based on organizational roles. Directly applicable to controlling who can query individual-level vs. aggregate organizational graph data.
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The Hidden Power of Social Networks - Rob Cross and Andrew Parker - Harvard Business Review Press (2004) - Chapter 7 addresses the ethical dimensions of network analysis in organizations, including transparency obligations, preventing surveillance misuse, and maintaining trust with employees.
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Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World - Bruce Schneier - W. W. Norton (2015) - Examines surveillance, data collection ethics, and the balance between organizational insight and individual privacy. Provides philosophical grounding for the ethical frameworks in this chapter.
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General Data Protection Regulation (GDPR) - Wikipedia - The EU's comprehensive data protection law establishing consent requirements, data minimization principles, and individual rights that constrain organizational analytics programs.
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Anonymization - Wikipedia - Techniques for removing personally identifiable information from datasets including k-anonymity, l-diversity, and t-closeness, essential for privacy-preserving organizational network analysis.
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Privacy by Design - Wikipedia - Framework for embedding privacy protections into system architecture from the outset rather than retrofitting. Core design principle for building ethical organizational analytics platforms.
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Encryption - Wikipedia - Overview of symmetric and asymmetric encryption methods for protecting data at rest and in transit, including TLS for network communication and AES for database encryption.
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NIST Cybersecurity Framework - NIST - U.S. federal framework for managing cybersecurity risk across identify, protect, detect, respond, and recover functions. Provides enterprise security context for organizational graph database deployments.