Mohammed Kaosar

With the incarnation of novel COVID-19, health care is getting more preference in each country. IoT-based health monitoring systems might be the best option to monitor infected patients and be helpful for elderly population. In this paper, analyzed different IoT-based health monitoring systems and their challenges. Searched through established journal and conference databases using specific keywords to find scholarly works to conduct the analysis. Investigated unique articles related to this analysis. The selected papers were then sifted through to understand their contributions/research focus. Then tried to find their research gap and challenges, created them into opportunities and proposed a GSM-based offline health monitoring system that will conduct with the healthcare providers through communication networks. Hopefully, this model will work as an absolute pathway for the researchers to establish a sustainable IoT-based health monitoring system for humankind.

An era of open information in the healthcare is now underway. This information can be considered as ‘Big data’, not only for its sheer volume but also for its complexity, diversity, and timeliness of data for any large eHealth System such as Personally Controlled Electronic Health Record (PCEHR). The system enables different person or organization to access, share, and manage their health data. Other challenges incorporated with the PCEHR data can be very excessive to capture, store, process and retrieve the insight knowledge in real time. Various PCEHR frameworks have been proposed in recent literature. However, big data challenges have not been considered in these frameworks. In this paper, we argue the PCEHR data should be considered as big data and the challenges of big data should be addressed when to design the framework of the PCEHR system. In doing so, we propose a PCEHR framework, which deals with real time big data challenges using the state-of-the-art technologies such as Apache Kafka and Apache Storm. At the same time the proposed framework ensures secure data communication using cryptographic techniques. Using a qualitative analysis, we show that the proposed framework addresses the big data challenges.

This paper presents a model for ensuring data integrity using anomalous node identification in non-homogeneous wireless sensor networks (WSNs). We propose the anomaly detection technique while collecting data using mobile data collectors (MDCs), which detect the malicious activities before sending to the base station (BS). Our technique also protects the leader nodes (LNs) from malicious activities to ensure data integrity between the MDC and the LNs. The proposed approach learns the data characteristics from each sensor node and passes it to the MDC, where detection engine identifies the victim node and eventually alarm the LNs in order to keep the normal behaviour in the network. Our empirical evidence shows the effectiveness our approach.

Oblivious Transfer(OT) protocol allows a client retrieving one or multiple records from a server without letting the server know about the choice of the client. OT has been one of the emerging research areas for last several years. There exist many practical applications of OT, especially in digital media subscription. In this paper, we propose a fully homomorphic encryption based secure k out of n oblivious transfer protocol. This novel protocol, first ever to use fully homomorphic encryption mechanism for integers numbers, allows the client choosing its desired records by sending encrypted indexes to the server, server works on encrypted indexes and sends back encrypted result without knowing which records the client was interested in. From the encrypted response of the server, the client only can decrypt its desired records. The security analysis demonstrates that, the desired security and privacy requirement of OT is ensured by the proposed protocol. Some optimizations are also introduced in the proposed solution to reduce transmission overhead.

Frequent Path tree (FP-tree) is a popular method to compute association rules and is faster than Apriori-based solutions in some cases. Association rule mining using FP-tree method cannot ensure entire privacy since frequency of the itemsets are required to share among participants at the first stage. Moreover, FP-tree method requires two scans of database transactions which may not be the best solution if the database is very large or the database server does not allow multiple scans. In addition, one-pass FP-tree can accommodate continuous or periodically changing databases without restarting the process as opposed to a regular FP-tree based solution. In this paper, the authors propose a one-pass FP-tree method to perform association rule mining without compromising any data privacy among two parties. A fully homomorphic encryption system over integer numbers is applied to ensure secure computation among two data sites without disclosing any number belongs to themselves.

Sensor devices provide sophisticated services in collecting data in various applications, some of which are privacy sensitive; others are ordinary. This chapter emphasizes the necessity and some mechanisms of privacy preserving data gathering techniques in wireless sensor network communication. It also introduces a new solution for privacy preserving data gathering in wireless sensor networks. By using perturbation technique in a semi-trusted server model, this new solution is capable of reducing a significant amount of computation in data collection process. In this technique, data of a sensor is perturbed into two components which are unified into two semi-trusted servers. Servers are assumed not to collude each other. Neither of them have possession of any individual data. Therefore, they cannot discover individual data. There are many real life applications in which the proposed model can be applied. Moreover, this chapter also shows a technique to collect grouped data from distributed sources keeping the privacy preserved. Security proofs show that any of the servers or any individual sensor neither can discover any individual data nor can associate any data to an individual sensor. Thus, the privacy of individual data is preserved.

In two party privacy preserving association rule mining, the issue to securely compare two integers is considered as the bottle neck to achieve maximum privacy. Recently proposed fully homomorphic encryption (FHE) scheme by Dijk et.al. can be applied in secure computation. Kaosar, Paulet and Yi have applied it in preserving privacy in two-party association rule mining, but its performance is not very practical due to its huge cyphertext, public key size and complex carry circuit. In this paper we propose some optimizations in applying Dijk et.al.’s encryption system to securely compare two numbers. We also applied this optimized solution in preserving privacy in association rule mining (ARM) in two-party settings. We have further enhanced the two party secure association rule mining technique proposed by Kaosar et.al. The performance analysis shows that this proposed solution achieves a significant improvement.