--- title: "CRM; security and privacy of data mining" subject: "Data Mining" chapter: 7 key: "semester_7/data_mining:crm-security-privacy" source: https://www.bctengineer.com/q/semester_7/data_mining/crm-security-privacy site: BCT Engineering --- # CRM; security and privacy of data mining Data Mining · Chapter 7: Advanced Applications - Asked in: 2066 Bhadra, 2+6 marks - Answer checked by Claude Opus 5.5 (high effort) on 2026-10-14 - Page: https://www.bctengineer.com/q/semester_7/data_mining/crm-security-privacy - Whole chapter: https://www.bctengineer.com/md/semester_7/data_mining/chapter-7 ## Question What is CRM? Describe the security and privacy aspects of data mining. ## Short answer CRM is a strategy for profitably acquiring, retaining, and serving customers by integrating marketing, sales, and service. Data mining raises privacy issues such as profiling, inference, and re-identification, plus security issues like unauthorized access and data poisoning. Protections include privacy-preserving mining, access control, anonymization, and legal rules such as GDPR. ## Answer #### CRM **Customer Relationship Management (CRM)** is a strategy, supported by technology, for acquiring, retaining and serving customers profitably by building long-term relationships. It integrates marketing, sales and customer service, and stores all customer interactions in a central database (data warehouse). Data mining on CRM data supports customer segmentation, churn prediction, cross-selling and targeted marketing. #### Security and privacy aspects of data mining Data mining works on large personal data and can discover sensitive facts, so it raises both security and privacy concerns. **Privacy issues** - Mining personal data (purchases, health, web use, location) without the knowledge or consent of the people involved. - **Profiling** and **inference**: combining data from many sources can reveal sensitive attributes (religion, health, income) that were never disclosed, even from anonymised data (re-identification). - **Misuse** of the results: discrimination, unwanted marketing, identity theft; the data may be sold to third parties. - Wrong or biased patterns may unfairly label a person. **Security issues** - Unauthorised access to the data warehouse and mined results; data breach. - Hackers or insiders can modify data (data poisoning). **Protection measures** 1. **Privacy-preserving data mining (PPDM)**: perturb data with noise, k-anonymity, differential privacy, secure multiparty computation, and distributed mining without sharing raw data. 2. **Access control** (authentication, roles), encryption of data in storage and transit, audit logs. 3. **Data minimisation and anonymisation** (remove identifiers). 4. **Legal and ethical rules**: informed consent, purpose limitation, privacy policy, and law, e.g. the Privacy Act, 2075 (2018) of Nepal, GDPR and similar laws elsewhere. 5. Use data mining only for the stated purpose, with transparency. ## Check yourself The answer's key points as multiple-choice questions, with the correct option and why. 1. What is CRM? A. A strategy supported by technology to acquire, retain and serve customers profitably through long-term relationships B. A database language for customer records C. A method to compress customer data D. A tool only for network security Answer: A. CRM integrates marketing, sales and service around a central customer database. (Key point: CRM definition) 2. How can anonymised data still threaten privacy? A. Combining it with data from other sources can re-identify people and reveal sensitive attributes B. Anonymised data is always encrypted C. Anonymisation increases the noise in the data D. It makes data mining impossible Answer: A. Linking multiple sources allows inference of undisclosed sensitive attributes and re-identification. (Key point: Re-identification) 3. What is a security issue where insiders or hackers modify data used for mining? A. Data minimisation B. Data poisoning C. k-anonymity D. Purpose limitation Answer: B. Tampering with the data is called data poisoning and corrupts mined results. (Key point: Data poisoning) 4. Which is a privacy-preserving data mining technique? A. Adding noise (perturbation) or differential privacy B. Collecting more personal attributes C. Selling results to third parties D. Removing access control to speed up mining Answer: A. PPDM uses perturbation, k-anonymity, differential privacy and secure multiparty computation. (Key point: Protection) ## Close variants set in other exams - [Short note: Security and privacy in DM](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Asecurity-privacy-note) — Data Mining, chapter 7 ## Related in Data Mining - [Short note: Privacy matters in CRM](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Acrm-privacy-note) — Data Mining, chapter 7 - [Short note: Data mining in CRM](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Acrm-data-mining-note) — Data Mining, chapter 7 - [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 - [Data mining vs DW and applications](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Adm-vs-dw-applications) — Data Mining, chapter 1 - [Data mining and its applications](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Adm-banking-biomedical) — Data Mining, chapter 1 - [Where data mining is applicable](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Adm-areas-applicable) — Data Mining, chapter 1 ## Related in other subjects - [Data warehouse and data mining](https://www.bctengineer.com/question.md?key=semester_8%2Finformation_system%3Adata-warehouse-data-mining) — Information System, chapter 4, asked 2× - [Short note: data mining and warehousing](https://www.bctengineer.com/question.md?key=semester_8%2Finformation_system%3Adata-mining-and-warehousing-note) — Information System, chapter 4 - [Data mining and business knowledge](https://www.bctengineer.com/question.md?key=semester_8%2Finformation_system%3Adata-mining-business-knowledge) — Information System, chapter 4