Privacy preserving publication of relational and transaction data: Survey on the anonymization of patient data

@article{Puri2019PrivacyPP,
  title={Privacy preserving publication of relational and transaction data: Survey on the anonymization of patient data},
  author={Vartika Puri and Shelly Sachdeva and Parmeet Kaur},
  journal={Comput. Sci. Rev.},
  year={2019},
  volume={32},
  pages={45-61}
}
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10 Citations
Background Citations
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10 Citations

Effective Removal of Privacy Breaches in Disassociated Transactional Datasets
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This paper addresses the problem of privacy-preserving publication of transactional data using two enhanced versions of ‘disassociation’ technique and proposes two algorithms for improvement in disassociation using suppression and addition (IDSA) and generalizing cover item (IDGC) to eliminate the cover problem of disassociated data.
A survey of privacy-preserving mechanisms for heterogeneous data types
Heterogeneous data release for cluster analysis with differential privacy
Transactional Data Anonymization for Privacy and Information Preservation via Disassociation and Local Suppression
TLDR
A novel vertical partition strategy that can retain more association between items in the dataset, which improves the utility of published data.
Recent Developments in Privacy-preserving Mining of Clinical Data
TLDR
Looking at dominant techniques and recent innovations to them, the applicability of these methods to the privacy-preserving analysis of clinical data is examined and promising directions for future research in this area are discussed.
DI-Mondrian: Distributed improved Mondrian for satisfaction of the L-diversity privacy model using Apache Spark
Impact of the Validity Analysis Model and Multirelational Data Clustering Based on the Trust Probability
TLDR
Investigations show that the validity analysis model of multirelational data clustering based on credible probability is more effective in practice and satisfies the research goals entirely.
Internet of Things and Communication Technology Synergy for Remote Services in Healthcare
TLDR
This chapter presents several aspects regarding IoT (Internet of Things) and communication technology synergies for remote services in healthcare for patients with higher risks that justify remote monitoring, taking into account the risks for data security that appears in the case of these applications.
Bridging unlinkability and data utility: Privacy preserving data publication schemes for healthcare informatics
( k , m , t )‐anonymity: Enhanced privacy for transactional data

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