Privacy-preserving data mining: methods, metrics, and applications

R Mendes, JP Vilela - IEEE Access, 2017 - ieeexplore.ieee.org
The collection and analysis of data are continuously growing due to the pervasiveness of
computing devices. The analysis of such information is fostering businesses and …

Privacy-preserving data publishing: A survey of recent developments

BCM Fung, K Wang, R Chen, PS Yu - ACM Computing Surveys (Csur), 2010 - dl.acm.org
The collection of digital information by governments, corporations, and individuals has
created tremendous opportunities for knowledge-and information-based decision making …

Data Mining The Text Book

C Aggarwal - 2015 - Springer
This textbook explores the different aspects of data mining from the fundamentals to the
complex data types and their applications, capturing the wide diversity of problem domains …

t-closeness: Privacy beyond k-anonymity and l-diversity

N Li, T Li, S Venkatasubramanian - 2007 IEEE 23rd …, 2006 - ieeexplore.ieee.org
The k-anonymity privacy requirement for publishing microdata requires that each
equivalence class (ie, a set of records that are indistinguishable from each other with respect …

[KİTAP][B] A general survey of privacy-preserving data mining models and algorithms

CC Aggarwal, PS Yu - 2008 - Springer
In recent years, privacy-preserving data mining has been studied extensively, because of
the wide proliferation of sensitive information on the internet. A number of algorithmic …

Pufferfish: A framework for mathematical privacy definitions

D Kifer, A Machanavajjhala - ACM Transactions on Database Systems …, 2014 - dl.acm.org
In this article, we introduce a new and general privacy framework called Pufferfish. The
Pufferfish framework can be used to create new privacy definitions that are customized to the …

Privacy: Theory meets practice on the map

A Machanavajjhala, D Kifer, J Abowd… - 2008 IEEE 24th …, 2008 - ieeexplore.ieee.org
In this paper, we propose the first formal privacy analysis of a data anonymization process
known as the synthetic data generation, a technique becoming popular in the statistics …

Llm-pbe: Assessing data privacy in large language models

Q Li, J Hong, C **e, J Tan, R **n, J Hou, X Yin… - arxiv preprint arxiv …, 2024 - arxiv.org
Large Language Models (LLMs) have become integral to numerous domains, significantly
advancing applications in data management, mining, and analysis. Their profound …

Overview and framework for data and information quality research

SE Madnick, RY Wang, YW Lee, H Zhu - Journal of data and information …, 2009 - dl.acm.org
Awareness of data and information quality issues has grown rapidly in light of the critical role
played by the quality of information in our data-intensive, knowledge-based economy …

K-isomorphism: privacy preserving network publication against structural attacks

J Cheng, AW Fu, J Liu - Proceedings of the 2010 ACM SIGMOD …, 2010 - dl.acm.org
Serious concerns on privacy protection in social networks have been raised in recent years;
however, research in this area is still in its infancy. The problem is challenging due to the …