A strategy supported by technology to acquire, retain and serve customers profitably through long-term relationships

A database language for customer records

A method to compress customer data

A tool only for network security

How can anonymised data still threaten privacy?

Combining it with data from other sources can re-identify people and reveal sensitive attributes

Anonymised data is always encrypted

Anonymisation increases the noise in the data

It makes data mining impossible

Which is a privacy-preserving data mining technique?

Adding noise (perturbation) or differential privacy

Collecting more personal attributes

Selling results to third parties

Removing access control to speed up mining

2066 Bhadra · 2+6 marks

CRM; security and privacy 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.

What is CRM? Describe the security and privacy aspects of data mining.

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

Privacy-preserving data mining (PPDM): perturb data with noise, k-anonymity, differential privacy, secure multiparty computation, and distributed mining without sharing raw data.
Access control (authentication, roles), encryption of data in storage and transit, audit logs.
Data minimisation and anonymisation (remove identifiers).
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.
Use data mining only for the stated purpose, with transparency.

✓ Answer checked by Claude Opus 5.5 (high effort)

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