θ-Sensitive k-Anonymity: An Anonymization Model for IoT based Electronic Health Records

Abstract

The Internet of Things (IoT) is an exponentially growing emerging technology, which is implemented in the digitization of Electronic Health Records (EHR). The application of IoT is used to collect the patient’s data and the data holders and then to publish these data. However, the data collected through the IoT-based devices are vulnerable to information leakage and are a potential privacy threat. Therefore, there is a need to implement privacy protection methods to prevent individual record identification in EHR. Significant research contributions exist e.g., p+-sensitive k-anonymity and balanced p+-sensitive k-anonymity for implementing privacy protection in EHR. However, these models have certain privacy vulnerabilities, which are identified in this paper with two new types of attack: the sensitive variance attack and categorical similarity attack. A mitigation solution, the θ -sensitive k-anonymity privacy model, is proposed to prevent the mentioned attacks. The proposed model works effectively for all k-anonymous size groups and can prevent sensitive variance, categorical similarity, and homogeneity attacks by creating more diverse k-anonymous groups. Furthermore, we formally modeled and analyzed the base and the proposed privacy models to show the invalidation of the base and applicability of the proposed work. Experiments show that our proposed model outperforms the others in terms of privacy security (14.64%).

A Practical Privacy-Preserving Publishing Mechanism Based on Personalized k-Anonymity and Temporal Differential Privacy for Wearable IoT Applications

“…L-diversity [15] and t-closeness [25] are proposed to prevent link attack and background knowledge attack. Razaullah Khan et al [26] propose the θ-Sensitive k-anonymity algorithm, in which θ is set as a threshold, distinguishing different diversity level of data and adding noise. It is proved that this algorithm can prevent the sensitive variance attack and categorical similarity attack.…”

Privacy-preserving healthcare informatics: a review

“…The anonymized database could be studied in place of the original database. Some common data anonymization models to prevent privacy disclosure include k-anonymity [10][11][12][13][14], l-diversity [9], t-closeness [15] and -presence [16]. a) k-anonymity: k-anonymity was developed to address identity disclosure.…”

“…In k-anonymity, any individual cannot be reidentified from the published data with a probability of higher than 1/k. Other variations of k-anonymity include clustering anonymity [11], distribution-preserving k-anonymity [12], optimizationbased k-anonymity [13], -sensitive-k-anonymity [14], (X,Y)-anonymity [17], (α, k)anonymity [18], LKC-privacy [19] and random k-anonymous [20] which prevent identity disclosure by hiding the record of a target in an equivalence class of records with the same QID values. Although k-anonymity model protects against identity disclosure, it is vulnerable against attribute disclosure.…”

(τ, m)‐slicedBucket privacy model for sequential anonymization for improving privacy and utility

“…This section investigates and categorize the available relevant research work in order to define narrow scope of the proposed work. For implementing privacy and security in IoT and Big Data, numerous techniques exist in literature [17][18][19][20][21][22][23][24][25][26]. For PPDP, a bunch of anonymization algorithms have also been proposed so far.…”

“…-diversity [8], t-closeness [9], -likeness [20], -Sensitive k-anonymity [21], are the syntactic approaches which prevent the linking attack. In all these privacy approaches the microdata is generalized into k-anonymized groups where every record is un-differentiable from at least k-1 other records.…”