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

  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.},
Effective Removal of Privacy Breaches in Disassociated Transactional Datasets
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.
Heterogeneous data release for cluster analysis with differential privacy
Transactional Data Anonymization for Privacy and Information Preservation via Disassociation and Local Suppression
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
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.
Impact of the Validity Analysis Model and Multirelational Data Clustering Based on the Trust Probability
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
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.
( k , m , t )‐anonymity: Enhanced privacy for transactional data


Efficient and flexible anonymization of transaction data
A rule-based privacy model is introduced that allows data publishers to express fine-grained protection requirements for both identity and sensitive information disclosure, and two anonymization algorithms are developed that significantly outperform the state-of-the-art in terms of retaining data utility, while achieving good protection and scalability.
Anonymizing Transaction Data to Eliminate Sensitive Inferences
This paper model potential inferences of individuals' identities and their associated sensitive transaction information as a set of implications, and designs an effective algorithm that is capable of anonymizing data to prevent these sensitive inferences with minimal data utility loss.
Privacy-preserving anonymization of set-valued data
A new version of the k-anonymity guarantee is defined, the km-Anonymity, to limit the effects of the data dimensionality and two efficient algorithms to transform the database are proposed.
Privacy-preserving heterogeneous health data sharing
The proposed algorithm allows the disclosure of health data containing both relational and set-valued data in a differentially private manner, and can retain essential information for discriminative analysis.
Anonymous Publication of Sensitive Transactional Data
This work proposes two categories of novel anonymization methods based on approximate nearest-neighbor (NN) search in high-dimensional spaces, which is efficiently performed through locality-sensitive hashing (LSH) and two data transformations that capture the correlation in the underlying data: reduction to a band matrix and Gray encoding-based sorting.
Anonymizing Data with Relational and Transaction Attributes
This work develops two frameworks to offer privacy, with bounded information loss in one attribute type and minimalInformation loss in the other, and proposes privacy algorithms that effectively preserve data utility, as verified by extensive experiments.
On k-Anonymity and the Curse of Dimensionality
It is shown that the curse of high dimensionality also applies to the problem of privacy preserving data mining, and when a data set contains a large number of attributes which are open to inference attacks, it becomes difficult to anonymize the data without an unacceptably high amount of information loss.
Local and global recoding methods for anonymizing set-valued data
A new version of the k-anonymity guarantee is defined, the km-Anonymity, to limit the effects of the data dimensionality, and an algorithm that finds the optimal solution is developed, however, at a high cost that makes it inapplicable for large, realistic problems.
COAT: COnstraint-based anonymization of transactions
COnstraint-based Anonymization of Transactions is proposed, an algorithm that anonymizes transactions using a flexible anonymization scheme to meet the specified constraints and is shown to be effective in preserving both privacy and utility in a real-world scenario that requires disseminating patients’ information.