PRIVACY PRESERVING IN DATA MINING (Professional Elective – V) B.Tech. IV Year I Sem. JNTUH R-18

• Explain the core challenges of privacy preservation in data mining and its importance.

• Compare and contrast different privacy-preserving data mining models and algorithms (e.g., k-anonymity, l-diversity, differential privacy).

• Analyze the Randomization Method for privacy preservation and its potential drawbacks.

• Discuss the concept of Group Based Anonymization and its application in protecting sensitive data.

• Explain the benefits and limitations of Distributed Privacy-Preserving Data Mining.

Unit II: Inference Control Methods

• Differentiate between perturbing and non-perturbing masking methods for data protection.

• Evaluate the effectiveness of various perturbing masking methods like k-anonymity and differential privacy.

• Discuss the principles of Synthetic Microdata Generation and its role in anonymization.

• Analyze the trade-off between information loss and disclosure risk in anonymization techniques.

• Explain the concept of reconstruction attacks and their implications for anonymity.

Unit III: Measures of Anonymity and Anonymization Methods

• Compare and contrast different statistical, probabilistic, and computational measures of anonymity.

• Analyze the strengths and weaknesses of various data anonymization methods like k-anonymity, l-diversity, and differential privacy.

• Evaluate the effectiveness of reconstruction methods for recovering anonymized data.

• Discuss the application of Randomization in anonymization and its impact on both privacy and utility.

Unit IV: Multiplicative Perturbation

• Explain the concept of Multiplicative Perturbation and its transformation-invariant properties.

• Analyze the privacy evaluation techniques for Multiplicative Perturbation and their limitations.

• Discuss approaches for designing Attack-Resilient Multiplicative Perturbation schemes.

• Explain different metrics for quantifying privacy level, hiding failure, and data quality in Multiplicative Perturbation.

• Compare and contrast Multiplicative Perturbation with other privacy-preserving methods.

Unit V: Utility-Based Privacy-Preserving Data

• Distinguish between privacy-preserving methods based on privacy-centric and utility-centric models.

• Analyze the effectiveness of Utility-Based Anonymization using Local Recording for preserving data utility.

• Discuss the application of Utility-Based Privacy Preserving Methods in classification problems and their challenges.

• Explain the concept of Anonymization Merging and its role in injecting utility into anonymized datasets.

• Compare and contrast different utility-based methods and their suitability for various data mining tasks.DSD