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Association rule hiding methods

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/11527503_89openalex-snapshot

A Further Study on Inverse Frequent Set Mining

inferredcites →https://doi.org/10.1007/11546849_41openalex-snapshot

Information Driven Evaluation of Data Hiding Algorithms

inferredcites →https://doi.org/10.1007/3-540-45496-9_27openalex-snapshot

Hiding Association Rules by Using Confidence and Support

inferredcites →https://doi.org/10.1007/978-0-387-70992-5openalex-snapshot

Privacy-Preserving Data Mining: Models and Algorithms

inferredcites →https://doi.org/10.1007/978-0-387-70992-5_8openalex-snapshot

A Survey of Quantification of Privacy Preserving Data Mining Algorithms

inferredcites →https://doi.org/10.1007/978-1-4419-6569-1openalex-snapshot

Association Rule Hiding for Data Mining

inferredcites →https://doi.org/10.1007/978-1-4419-6569-1_1openalex-snapshot

Introduction

inferredcites →https://doi.org/10.1007/978-3-540-24775-3_10openalex-snapshot

Secure Association Rule Sharing

inferredcites →https://doi.org/10.1007/s10115-008-0178-7openalex-snapshot

Hiding sensitive knowledge without side effects

inferredcites →https://doi.org/10.1007/s10618-005-0006-6openalex-snapshot

A Framework for Evaluating Privacy Preserving Data Mining Algorithms*

inferredcites →https://doi.org/10.1007/s10619-007-7013-0openalex-snapshot

Efficient algorithms for distortion and blocking techniques in association rule hiding

inferredcites →https://doi.org/10.1016/j.datak.2007.06.012openalex-snapshot

A MaxMin approach for hiding frequent itemsets

inferredcites →https://doi.org/10.1016/j.dss.2006.08.007openalex-snapshot

Dare to share: Protecting sensitive knowledge with data sanitization

inferredcites →https://doi.org/10.1023/a:1009796218281openalex-snapshot

Levelwise Search and Borders of Theories in Knowledge Discovery

inferredcites →https://doi.org/10.1109/icdm.2002.1183878openalex-snapshot

Proceedings 2002 IEEE International Conference on Data Mining. ICDM 2002

inferredcites →https://doi.org/10.1109/icdm.2003.1250990openalex-snapshot

Protecting sensitive knowledge by data sanitization

inferredcites →https://doi.org/10.1109/icdm.2005.2openalex-snapshot

A Border-Based Approach for Hiding Sensitive Frequent Itemsets

inferredcites →https://doi.org/10.1109/icdm.2005.27openalex-snapshot

Approximate inverse frequent itemset mining: privacy, complexity, and approximation

inferredcites →https://doi.org/10.1109/icdm.2005.67openalex-snapshot

Proceedings. Fifth IEEE International Conference on Data Mining

inferredcites →https://doi.org/10.1109/icdmw.2006.8openalex-snapshot

A Max-Min Approach for Hiding Frequent Itemsets

inferredcites →https://doi.org/10.1109/ideas.2003.1214911openalex-snapshot

Algorithms for balancing privacy and knowledge discovery in association rule mining

inferredcites →https://doi.org/10.1109/ideas.2005.2openalex-snapshot

Proceedings. 9th International Database Engineering and Applications Symposium (IDEAS 2005)

inferredcites →https://doi.org/10.1109/kdex.1999.836532openalex-snapshot

Disclosure limitation of sensitive rules

inferredcites →https://doi.org/10.1109/ride.2002.995109openalex-snapshot

Privacy preserving association rule mining

inferredcites →https://doi.org/10.1109/tkde.2004.1269668openalex-snapshot

Association rule hiding

inferredcites →https://doi.org/10.1109/tkde.2007.250583openalex-snapshot

Hiding Sensitive Association Rules with Limited Side Effects

inferredcites →https://doi.org/10.1109/tkde.2008.199openalex-snapshot

Exact Knowledge Hiding through Database Extension

inferredcites →https://doi.org/10.1145/1055558.1055580openalex-snapshot

Computational complexity of itemset frequency satisfiability

inferredcites →https://doi.org/10.1145/1183614.1183721openalex-snapshot

An integer programming approach for frequent itemset hiding

inferredcites →https://doi.org/10.1145/1755688.1755726openalex-snapshot

K-anonymous association rule hiding

inferredcites →https://doi.org/10.1145/335191.335438openalex-snapshot

Privacy-preserving data mining

inferredcites →https://doi.org/10.1145/342009.335438openalex-snapshot

Privacy-preserving data mining

inferredcites →https://doi.org/10.1145/603867.603875openalex-snapshot

XML document versioning

inferredcites →https://doi.org/10.1145/604264.604271openalex-snapshot

Using unknowns to prevent discovery of association rules

inferredcites →https://doi.org/10.1145/773153.773181openalex-snapshot

Feasible itemset distributions in data mining

inferredcites →https://doi.org/10.1145/974121.974131openalex-snapshot

State-of-the-art in privacy preserving data mining

inferredcites →https://doi.org/10.1287/isre.1050.0056openalex-snapshot

Maximizing Accuracy of Shared Databases when Concealing Sensitive Patterns

inferredcites →https://doi.org/10.1287/mnsc.1060.0603openalex-snapshot

Minimizing Information Loss and Preserving Privacy

inferredcites →https://doi.org/10.1504/ijbidm.2006.009135openalex-snapshot

A unified framework for protecting sensitive association rules in business collaboration

inferredcites →https://doi.org/10.3233/jcs-2000-8403openalex-snapshot

Using sample size to limit exposure to data mining

inferredcites →https://doi.org/10.5626/jcse.2007.1.1.074openalex-snapshot

Hiding Sensitive Frequent Itemsets by a Border-Based Approach

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/widm.1137openalex-snapshot

A survey of erasable itemset mining algorithms

inferred← citeshttps://doi.org/10.1007/978-3-030-80571-5_4openalex-snapshot

A Database Reconstruction Approach for the Inverse Frequent Itemset Mining Problem

inferred← citeshttps://doi.org/10.1007/978-3-031-64076-6_22openalex-snapshot

SMASK: Parallel Probabilistic Privacy-Preserving Frequent Pattern Mining Technique for Big Data

inferred← citeshttps://doi.org/10.1007/978-3-319-07455-9_9openalex-snapshot

Distortion-Based Heuristic Sensitive Rule Hiding Method – The Greedy Way

inferred← citeshttps://doi.org/10.1007/978-3-319-13186-3_9openalex-snapshot

Privacy Preserving Association Rule Mining Using Binary Encoded NSGA-II

inferred← citeshttps://doi.org/10.1007/978-3-319-27051-7_6openalex-snapshot

ARM-Based Privacy Preserving for Medical Data Publishing

inferred← citeshttps://doi.org/10.1007/978-3-319-57358-8_3openalex-snapshot

On the Classification of Protection Procedures

inferred← citeshttps://doi.org/10.1007/978-3-319-77965-2_1openalex-snapshot

The Conflict Between Big Data and Individual Privacy

inferred← citeshttps://doi.org/10.1007/s10489-015-0671-0openalex-snapshot

Privacy preservation through a greedy, distortion-based rule-hiding method

inferred← citeshttps://doi.org/10.1007/s10489-019-01524-2openalex-snapshot

Effective sanitization approaches to protect sensitive knowledge in high-utility itemset mining

inferred← citeshttps://doi.org/10.1016/j.eswa.2017.10.048openalex-snapshot

Data sanitization in association rule mining: An analytical review

inferred← citeshttps://doi.org/10.1016/j.jksuci.2020.06.010openalex-snapshot

A hybrid-security model for privacy-enhanced distributed data mining

inferred← citeshttps://doi.org/10.1016/j.pmcj.2019.03.001openalex-snapshot

A survey of privacy enhancing technologies for smart cities

inferred← citeshttps://doi.org/10.1080/1206212x.2024.2307086openalex-snapshot

Privacy-preserving association rule mining: a survey of techniques for sensitive rule identification and enhanced data protection

inferred← citeshttps://doi.org/10.1109/access.2014.2362522openalex-snapshot

Information Security in Big Data: Privacy and Data Mining

inferred← citeshttps://doi.org/10.1109/access.2017.2706947openalex-snapshot

Privacy-Preserving Data Mining: Methods, Metrics, and Applications

inferred← citeshttps://doi.org/10.1109/icicis52592.2021.9694174openalex-snapshot

The Generic Framework of Privacy Preserving Data Mining Phases: Challenges & Future Directions

inferred← citeshttps://doi.org/10.1109/ictai.2014.119openalex-snapshot

An Integer Linear Programming Scheme to Sanitize Sensitive Frequent Itemsets

inferred← citeshttps://doi.org/10.1109/ictai50040.2020.00188openalex-snapshot

Parallel based Hiding of Sensitive Knowledge

inferred← citeshttps://doi.org/10.1109/ictai59109.2023.00037openalex-snapshot

Graph based Hiding of Sensitive Knowledge

inferred← citeshttps://doi.org/10.1145/3502398openalex-snapshot

Applied Affective Computing

inferred← citeshttps://doi.org/10.1145/3502398.3502399openalex-snapshot

Preface

inferred← citeshttps://doi.org/10.1145/3502398.3502400openalex-snapshot

Introduction to Applied Affective Computing

inferred← citeshttps://doi.org/10.1145/3502398.3502401openalex-snapshot

Emotions as Studied in Psychology and Cognitive Science

inferred← citeshttps://doi.org/10.1145/3502398.3502402openalex-snapshot

Machine Learning Approaches for Applied Affective Computing

inferred← citeshttps://doi.org/10.1145/3502398.3502403openalex-snapshot

Multimodal Data Collection and Processing for Applied Affective Computing

inferred← citeshttps://doi.org/10.1145/3502398.3502404openalex-snapshot

Emotion Recognition in the Wild

inferred← citeshttps://doi.org/10.1145/3502398.3502405openalex-snapshot

Reinforcement Learning and Affective Computing

inferred← citeshttps://doi.org/10.1145/3502398.3502406openalex-snapshot

Synthesizing Natural and Believable Emotional Expressions

inferred← citeshttps://doi.org/10.1145/3502398.3502407openalex-snapshot

Emotion-aware Human–Robot Interaction and Social Robots

inferred← citeshttps://doi.org/10.1145/3502398.3502408openalex-snapshot

Affective Computing for Enhancing Well-Being

inferred← citeshttps://doi.org/10.1145/3502398.3502409openalex-snapshot

Applied Affective Computing in Built Environments

inferred← citeshttps://doi.org/10.1145/3502398.3502410openalex-snapshot

Addressing Ethical Issues of Affective Computing

inferred← citeshttps://doi.org/10.1145/3502398.3502411openalex-snapshot

Future of Affective Computing and Applied Affective Computing

inferred← citeshttps://doi.org/10.1145/3502398.3502412openalex-snapshot

Bibliography

inferred← citeshttps://doi.org/10.1145/3502398.3502413openalex-snapshot

Authors’ Biographies & Index

inferred← citeshttps://doi.org/10.1287/isre.2019.0898openalex-snapshot

Hiding Sensitive Information when Sharing Distributed Transactional Data

inferred← citeshttps://doi.org/10.1371/journal.pone.0127834openalex-snapshot

Use HypE to Hide Association Rules by Adding Items

inferred← citeshttps://doi.org/10.14569/ijacsa.2014.051126openalex-snapshot

A Comparison of the Effects of K-Anonymity on Machine Learning Algorithms

inferred← citeshttps://doi.org/10.1587/transinf.2014edp7345openalex-snapshot

Hide Association Rules with Fewer Side Effects

inferred← citeshttps://doi.org/10.18280/isi.240106openalex-snapshot

Efficient Method in Association Rule Hiding for Privacy Preserving with Data Mining Approach

inferred← citeshttps://doi.org/10.21090/ijaerd.c1087305openalex-snapshot

A SURVEY ON ASSOCIATION RULE HIDING APPROACHES

inferred← citeshttps://doi.org/10.2991/assehr.k.210519.238openalex-snapshot

A Study on Knowledge Hiding: Visualized Quantitative Analysis Based on Note Express

inferred← citeshttps://doi.org/10.31652/2415-7872-2025-82-15-20openalex-snapshot

МОЖЛИВОСТІ ВПРОВАДЖЕННЯ ШТУЧНОГО ІНТЕЛЕКТУ У ВИВЧЕННЯ ВІЙСЬКОВОЇ ТЕРМІНОЛОГІЇ

inferred← citeshttps://doi.org/10.3233/ida-160817openalex-snapshot

Association rule hiding based on evolutionary multi-objective optimization

inferred← citeshttps://doi.org/10.3233/ida-173613openalex-snapshot

A novel approach for hiding sensitive utility and frequent itemsets

inferred← citeshttps://doi.org/10.32628/ijsrset196368openalex-snapshot

Survey on Association Rule Hiding Techniques

inferred← citeshttps://doi.org/10.3390/su122410571openalex-snapshot

Security and Privacy in Big Data Life Cycle: A Survey and Open Challenges

inferred← citeshttps://doi.org/10.5120/14177-2357openalex-snapshot

A Survey on Association Rule Hiding Methods

inferred← citeshttps://doi.org/10.5120/18111-9240openalex-snapshot

Presenting a Hiding Algorithm for Improving Privacy Preserving in Association Rule Mining

inferred← citeshttps://doi.org/10.5120/ijais2017451717openalex-snapshot

Privacy Preserving Informative Association Rule Mining

inferred← citeshttps://doi.org/10.5829/idosi.jaidm.2016.04.02.10openalex-snapshot

Introducing an algorithm for use to hide sensitive association rules through perturb technique

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