---
title: "Short note: Security and privacy in DM"
subject: "Data Mining"
chapter: 7
key: "semester_7/data_mining:security-privacy-note"
source: https://www.bctengineer.com/q/semester_7/data_mining/security-privacy-note
site: BCT Engineering
---

# Short note: Security and privacy in DM

Data Mining · Chapter 7: Advanced Applications

- Asked in: year not given, 3 marks
- Video: [PRIVACY PRESERVING DATA MINING](https://www.youtube.com/watch?v=DbiYxapSi3k) — Ali Hussain Mohammad Ali Hussain
- Answer checked by Claude Opus 5.5 (high effort) on 2026-10-14
- Page: https://www.bctengineer.com/q/semester_7/data_mining/security-privacy-note
- Whole chapter: https://www.bctengineer.com/md/semester_7/data_mining/chapter-7

## Question

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

## 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.

## Answer

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.

## Check yourself

The answer's key points as multiple-choice questions, with the correct option and why.

1. How can non-sensitive data create a privacy problem?
   A. Patterns drawn from it can reveal sensitive facts
   B. It is automatically deleted
   C. It cannot be linked to anything
   D. It is always public
   Answer: A. Inference from non-sensitive data and re-identification by linking threaten privacy. (Key point: Inference)

2. Which is a security aspect of data mining?
   A. Informed consent
   B. Tampering with data or models (data poisoning)
   C. Purpose limitation
   D. Profiling
   Answer: B. Tampering is a security threat; the others are privacy or remedy items. (Key point: Security)

3. Which is a privacy-preserving data mining method?
   A. Selling data to partners
   B. Data perturbation, k-anonymity or differential privacy
   C. Storing all data forever
   D. Shared passwords
   Answer: B. PPDM includes perturbation, k-anonymity, differential privacy and secure multiparty methods. (Key point: Solutions)

## Close variants set in other exams

- [CRM; security and privacy of data mining](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Acrm-security-privacy) — Data Mining, chapter 7

## Related in Data Mining

- [Web structure/content/usage mining; privacy](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Aweb-structure-content-usage-privacy) — Data Mining, chapter 7
- [Short note: Architectural aspects of DM](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Aarchitectural-aspects-dm-note) — Data Mining, chapter 7