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Short note: Security and privacy in DM

Short answer

Data mining can infer sensitive facts from non-sensitive data and re-identify anonymised records, causing privacy risks. Solutions include privacy-preserving techniques such as perturbation, k-anonymity and differential privacy, plus access control, encryption, data minimisation, and compliance with laws like GDPR or Nepal's Privacy Act.

Write a short note on Security and Privacy Aspects of Data Mining.

Data mining works on large collections of personal and business data. It can discover sensitive knowledge, so security and privacy must be considered.

Privacy aspects

  • Personal data (shopping, health, finances, web use) is collected, often without the individual's knowledge or consent.
  • Inference: patterns drawn from non-sensitive data can reveal sensitive facts, and anonymised data can be re-identified by linking with other data.
  • Profiling can lead to unfair discrimination, wrong decisions and loss of reputation.
  • Misuse and sale of data and mined results to third parties; use beyond the original purpose.

Security aspects

Unauthorised access, data breaches, tampering with data or models (data poisoning), and attacks on the data warehouse.

Solutions

  1. Privacy-preserving data mining: data perturbation (adding noise), k-anonymity, differential privacy, cryptographic and secure multiparty methods, distributed mining without sharing raw data.
  2. Access control and encryption: authentication, role-based access, audit trail.
  3. Data minimisation and anonymisation; storing only what is needed.
  4. Law and ethics: informed consent, purpose limitation, transparency, and compliance with data protection laws (e.g. the Privacy Act, 2075 of Nepal; GDPR in the EU).

A balance is needed between the benefits of mining and the protection of individual rights.

✓ Answer checked by Claude Opus 5.5 (high effort)

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