An efficient privacy mechanism for electronic health records

Anjum, Adeel; Malik, Saif Ur Rehman; Choo, Kim‐Kwang Raymond; Khan, Abid; Haroon, Asma; Khan, Sangeen; Khan, Samee U.; Ahmad, Naveed; Raza, Basit

doi:10.1016/j.cose.2017.09.014

Cited by 53 publications

(47 citation statements)

References 12 publications

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“…The Petri Nets are modeling tools used for the graphical and mathematical modeling of various systems that can be characteristically concurrent, asynchronous, distributed, parallel, non-deterministic, or stochastic [13]. In this work, a variant of the classical Petri Nets, namely the High-level Petri Nets (HLPNs) [13], have been used to model the proposed schemes. Relevant details on the Petri Nets have been presented in [14].…”

Section: High-level Petri Netsmentioning

confidence: 99%

“…Definition 1 (HLPN) [13]. A HLPN can be defined as a 7-tuple, N = (P, T, F, φ, R, L, M 0 ), where the variable:…”

Section: High-level Petri Netsmentioning

confidence: 99%

“…The proposed schemes base their models on the time-series data of busses' connectivity collected over a period of time to forecast the future contact opportunities. We present the formal modeling and verification of the proposed schemes in the High-level Petri Nets (HLPNs) and the Z specification language [13,14]. The HLPN furnish the (a) detailed mathematical analyses of the communication processes undergoing in the proposed schemes and (b) comprehensive overview of the components and information flow encompassing data offloading.…”

Section: Introductionmentioning

confidence: 99%

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Data offloading in IoT environments: modeling, analysis, and verification

Ghosh

Khalid

Rais

et al. 2019

J Wireless Com Network

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Recent years have seen a significant growth in Internet of Things (IoT) technology consisting of a large number of devices embedded with sensors and deployed to perform monitoring and actuation tasks. The IoT devices collect large volumes of data that is usually uploaded to cloud to perform analytics and predictions. One of the main challenges in IoT is the transportation of large-scale data collected over a period of time at a remote site. Cellular networks are already facing explosive growth of mobile data traffic due to the proliferation of smart devices and traffic-intensive applications. An alternate solution is to perform the data offloading, where a portion of data can be offloaded from primary links and transferred using opportunistic terminal-to-terminal (T2T) network that relies on direct communication between mobile users, without any need for an infrastructure backbone. However, such approach may lead to data loss and delay if dynamics of time-varying topology and mobility of nodes is not taken care of. To address this challenge, we propose three prediction-based offloading schemes that exploit the mobility patterns and temporal contacts of nodes to predict future data transfer opportunities. We have utilized the High-level Petri Nets to model and formally analyzed the communication processes of the proposed schemes. The new symbolic model verifier (NuSMV) has been employed for the verification of the three schemes against the identified constraints. The verification results affirm the correctness and scalability of the models. The protocols are evaluated with performance metrics, such as the delivery ratio, latency, and overhead. Our results indicate significant improvement in performance compared to existing approaches.

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Section: High-level Petri Netsmentioning

confidence: 99%

“…Definition 1 (HLPN) [13]. A HLPN can be defined as a 7-tuple, N = (P, T, F, φ, R, L, M 0 ), where the variable:…”

Section: High-level Petri Netsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data offloading in IoT environments: modeling, analysis, and verification

Ghosh

Khalid

Rais

et al. 2019

J Wireless Com Network

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“…Where, C 1 and C 2 are ciphertext images before and after one pixel changes in a plaintext image. In the proposed approach, the fitness function that is used to measure the performance of each individual chromosome is defined using the Equation (12).…”

Section: Fitness Functionmentioning

confidence: 99%

“…Accordingly, ensuring the privacy of these digital records requires additional security measures. In this regard, a broad range of parameters need to be taken into account when designing a secure framework [11][12][13], including availability, integrity, authorization, confidentiality, data ownership, authentication and anonymization. To this purpose, various approaches and schemes are suggested to secure data processing in an untrusted cloud environment.…”

Section: Introductionmentioning

confidence: 99%

A Cloud-based Framework to Secure Medical Image Processing

Marwan

Kartit

Ouahmane

2018

JMM

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In the last few years, advanced software for processing medical images has gained a great interest in modern medicine. In fact, it provides valuable clinical information, and hence, can significantly improve diagnosis and treatment. Nevertheless, implementing these imaging tools often requires an important capital budget in both IT applications and hardware. This solution can unfortunately cause a dramatic increase in operational expenses and medical costs. To mitigate this problem, medical providers are shifting their interest onto using cloud computing, particularly the Software-as-a-Service (SaaS) model, instead of in-house data centres. In this case, healthcare professionals rely on remote applications delivered by an external provider to process patients' digital records. Interestingly, in this paradigm, consumers are billed based on software utilization. Besides, cloud computing promises to offer a better Quality of Service (QoS), including availability, elasticity, trust, response time, security assurance, etc. Regardless of its significant financial benefits, the transition to the cloud environment gives rise to security and privacy problems, especially in the healthcare domain. Recently, various security measures

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

Khan

Tao

Anjum

et al. 2020

Trans Emerging Tel Tech

Self Cite

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In a real-world scenario for privacy-preserving data publishing, the original data is anonymized and released periodically. Each release may vary in number of records due to insert, update, and delete operations. An intruder can combine i.e. correlate different releases to compromise the privacy of the individual records. Most of the literature, such as -safety, -safe (l, k)-diversity, have an inconsistency in record signatures and adds counterfeit tuples with high generalization that causes privacy breach and information loss. In this paper, we propose an improved privacy model ( , )-slicedBucket, having a novel idea of "Cache" table to address these limitations. We indicate that a collusion attack can be performed for breaching the privacy of -safe (l, k)-diversity privacy model, and demonstrate it through formal modeling. The objective of the proposed ( , )-slicedBucket privacy model is to set a tradeoff between strong privacy and enhanced utility. Furthermore, we formally model and analyze the proposed model to show that the collusion attack is no longer applicable. Extensive experiments reveal that the proposed approach outperforms the existing models.

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An efficient privacy mechanism for electronic health records

Cited by 53 publications

References 12 publications

Data offloading in IoT environments: modeling, analysis, and verification

Data offloading in IoT environments: modeling, analysis, and verification

A Cloud-based Framework to Secure Medical Image Processing

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

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