Data transformation for privacy-preserving data mining
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Summary
This thesis proposes a unified framework for privacy-preserving data mining that ensures that the mining process will not violate privacy up to a certain degree of security, and demonstrates the practicality and feasibility of achieving privacy preservation in data mining.
- Type
- article
- Published
- 2005-01-01
- Cited by
- 14
- References
- 99
- OpenAlex
- https://openalex.org/W160533925
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:1639542
Keywords
Computer science, Association rule learning, Information privacy, Data sharing, Data mining
References
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- A methodology for hiding knowledge in databases
- Finding Groups in Data: An Introduction to Cluster Analysis
- Clustering for edge-cost minimization (extended abstract)
- Fast Algorithms for Mining Association Rules in Large Databases
- An Algorithm for Protecting Knowledge Discovery Data
- From Data Mining to Knowledge Discovery: An Overview
- Attitudes Towards Information Privacy
- "How Did They Get My Name?": An Exploratory Investigation of Consumer Attitudes Toward Secondary Information Use
- SECURITY AND PRIVACY IMPLICATIONS OF DATA MINING
- Discovery, Analysis, and Presentation of Strong Rules
- A Taxonomy of Obfuscating Transformations
Cited by
- Enforcing privacy via access control and data perturbation.
- A probablistic framework for classification and fusion of remotely sensed hyperspectral data
- Privacy-Preserving Clustering to Uphold Business Collaboration: A Dimensionality Reduction Based Transformation Approach
- A unified framework for protecting sensitive association rules in business collaboration
- A privacy-preserving clustering approach toward secure and effective data analysis for business collaboration
- Hybrid SVD BASED DATA TRANSFORMATION METHODS FOR PRIVACY PRESERVING
- Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms
- A Case Study on Mining Security Issues & Remedies in Privacy Preservation
- Improved secure PCA and LDA algorithms for intelligent computing in IoT‐to‐cloud setting
- Reviewing advancements in privacy-enhancing technologies for big data analytics in an era of increased surveillance
- SANITIZING SENSITIVE ASSOCIATION RULES USING FUZZY CORRELATION SCHEME
- Privacy preserving data mining
- SAFETY ISSUES IN DATA MINING
- A Cryptographically Secure Scheme for Preserving Privacy in Association Rule Mining
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