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Cloudlets help in overcoming latency issue of clouds in mobile cloud computing to offload the computing tasks. Communication protocols are important part of the implementation of cloudlet-based systems for the mobile-cloudlet-cloud environment. In this work, an approach for communication between entities in a decentralised cloudlet-based system is proposed. For that purpose, a cloudlet discovery protocol which is used for discovering cloudlets in Wi-Fi vicinity of mobile devices is proposed. A selection algorithm for selecting the suitable cloudlet from available discovered cloudlets is also proposed. Our proposed selection algorithm uses infrastructure specific criterion for selection decision, which makes the algorithm more generic to use.

In today’s world, life-threatening diseases have become a pre-eminent issue in healthcare due to the higher mortality rate. It is possible to lower this mortality rate by utilizing healthcare intelligence to detect diseases early. Patient’s medical data is stored in the EHR system, which is kept up to date by the healthcare provider. Data mining techniques like Association Rule Mining can detect a patient’s disease from their symptoms using digital healthcare data stored in the EHR system. Association rule mining’s efficacy can be improved by using global data from various EHR systems. It mandates that all EHR systems exchange healthcare records to a central server. When personal health information is made available on an untrusted server, several privacy laws may be violated. As a result, the challenge of privacy preserving distributed healthcare data mining has become a well-known study field in the healthcare industry. This research uses an efficient ElGamal homomorphic encryption technique to protect privacy in a distributed association rule mining. The proposed approach to discover the risk factor of most life-threatening diseases like breast cancer and heart disease with its symptoms and discuss the scope for combating COVID-19. Theoretical analysis of the proposed approach shows that it is efficient and maintains privacy in an insecure communication environment. An experimental study with a real dataset shows the proposed approach’s benefit compared to the local single EHR system results.

The rank value of a node in Routing Protocol for Low Power and Lossy Networks (RPL) plays a significant role in preventing loops and route optimization. Advertising a fake rank value creates a false topology and can degrade network performance to a great extent. This manuscript aims to study the effects of increased and decreased variants of the rank attack on the RPL performance. We consider a smart home network topology to evaluate the RPL performance, and the simulation experiments are performed using the Cooja simulator. Through this study, we portray a rational analysis and discuss how the network performance can be degraded by each variant of the rank attack and the position of the attacker node in the network topology. The simulation results obtained in our work demonstrate how each variant of the rank attack can disrupt the default RPL working. We notice significant effects of both the studied rank attacks on the crucial parameters like packet overhead, average inter-packet time, packet delivery ratio and average power consumption. Researchers working in this field can take advantage of our research findings and extend their study to develop new defence mechanisms against these attacks.

The widespread usage of location based services (LBS); where obtaining any informational service is entirely based upon the user's current location, have raised a significant concern about location privacy of the user. For the queries like `where is my closest ATM?', `where is my nearest hospital', or any route assistance in general, it is essential to submit the user's actual location to avail the demanded services. Similar communication holds true for a location based vehicular transportation system. Cloaking and ob-fuscation are the two generalized approaches to deal with location privacy preservation in LBS. These approaches are mainly based on a trusted third party (TTP) and exploits the well established K-anonymity principle in order to make the query issuer indistinguishable with other K - 1 more users. In such approaches all the data (mainly location coordinates and queries) becomes available at the central server, thus complete knowledge of the query (including user id) exists at central node. This is the major limitation of TTP based architecture and makes such frameworks susceptible to different privacy attacks. This paper is a research attempt to extend the realm of collaborative communication among peers belonging to a mobile user group in a decentralized or trusted third party free architecture. We propose a collaborative P2P communication model; called CAST, that employs the series of trust among peers and peers use their cached mobile data to collaborate with each other in order to get the results locally. The scheme provides results locally with low latency and works efficiently when the peers share common inclinations (or data value). The proposed algorithm preserves user's privacy and performs effectively under pull-based sporadic query scenario.

Association rule mining can be used in healthcare data mining to provide solutions to life-threatening diseases like recent COVID-19. Due to healthcare data privacy concerns, privacy preserving distributed healthcare data mining becomes the primary focus of medical science research. Recently, Chahar et al. (Sādhanā 42:1997–2007, 2017) proposed privacy preserving distributed association rule mining scheme with insecure communication channels. They used the concept of an elliptic curve-based paillier cryptosystem to achieve privacy, authenticity, and integrity. We observed some security vulnerabilities in their privacy preserving association rule mining scheme when implemented with insecure communication channels. We observed that the security vulnerabilities will result in the disclosure of private data of sites (or participants). Furthermore, we propose a secure version of their scheme to solve the security vulnerabilities with insecure communication channels. Theoretical and experimental analysis shows that the proposed scheme has almost equal computation and communication complexities with better securities. A case study on the effectiveness of the proposed approach in combating COVID-19 coronavirus and Breast Cancer is also discussed.