Privacy-Preserving Data Mining Models and Algorithms /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Aggarwal, Charu C. (Επιμελητής έκδοσης), Yu, Philip S. (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Boston, MA : Springer US, 2008.
• Σειρά: Advances in Database Systems, 34
• Θέματα: Computer science.; Computer security.; Data encryption (Computer science).; Database management.; Data mining.; Information storage and retrieval.; Computer Science.; Systems and Data Security.; Data Mining and Knowledge Discovery.; Data Encryption.; Database Management.; Information Storage and Retrieval.; Information Systems Applications (incl. Internet).

Advances in hardware technology have increased the capability to store and record personal data about consumers and individuals. This has caused concerns that personal data may be used for a variety of intrusive or malicious purposes. Privacy Preserving Data Mining: Models and Algorithms proposes a...

Πίνακας περιεχομένων:

• An Introduction to Privacy-Preserving Data Mining
• A General Survey of Privacy-Preserving Data Mining Models and Algorithms
• A Survey of Inference Control Methods for Privacy-Preserving Data Mining
• Measures of Anonymity
• k-Anonymous Data Mining: A Survey
• A Survey of Randomization Methods for Privacy-Preserving Data Mining
• A Survey of Multiplicative Perturbation for Privacy-Preserving Data Mining
• A Survey of Quantification of Privacy Preserving Data Mining Algorithms
• A Survey of Utility-based Privacy-Preserving Data Transformation Methods
• Mining Association Rules under Privacy Constraints
• A Survey of Association Rule Hiding Methods for Privacy
• A Survey of Statistical Approaches to Preserving Confidentiality of Contingency Table Entries
• A Survey of Privacy-Preserving Methods Across Horizontally Partitioned Data
• A Survey of Privacy-Preserving Methods Across Vertically Partitioned Data
• A Survey of Attack Techniques on Privacy-Preserving Data Perturbation Methods
• Private Data Analysis via Output Perturbation
• A Survey of Query Auditing Techniques for Data Privacy
• Privacy and the Dimensionality Curse
• Personalized Privacy Preservation
• Privacy-Preserving Data Stream Classification.