The recent proliferation of smart home environments offers new and transformative circumstances for various domains with a commitment to enhancing the quality of life and experience of their inhabitants. However, most of these environments combine different gadgets offered by multiple stakeholders in a dynamic and decentralized manner, which in turn presents new challenges from the perspective of digital investigation. In addition, a plentiful amount of data records got generated because of the day-to-day interactions between smart home's gadgets and homeowners, which poses difficulty in managing and analyzing such data. The analysts should endorse new digital investigation approaches and practices to tackle the current limitations in traditional digital investigations when used in these environments. The digital evidence in such environments can be found inside the records of log-files that store the historical events and various actions occurred inside the smart home. Threat hunting can leverage the collective nature of these gadgets, the vengeful artifacts observed on smart home environments can be shared between each other to gain deeper insights into the best way for responding to new threats, which in turn can be valuable in reducing the impact of breaches. Nevertheless, this approach depends mainly on the readiness of smart homeowners to share their own personal usage logs that have been extracted from their smart home environments. However, they might disincline to employ such service due to the sensitive nature of the information logged by their personal gateways. In this paper, we presented an approach to enable smart homeowners to share their usage logs in a privacypreserving manner. A distributed threat hunting approach has been developed to elicit the various threat reputations with effective privacy guarantees. The proposed approach permits the composition of diverse threat classes without revealing the logged records to other involved parties. Furthermore, a scenario was proposed to depict a proactive threat Intelligence sharing for the detection of potential threats in smart home environments with some experimental results.
Recommending support-groups in healthcare social networks is the problem of detecting for each patient his/her membership to one support-group of relevant patients. The patients in each support-group share some relevant preferences which guarantee that the support-group as a whole satisfies some desired properties of similarity. As a result, forming these support-groups requires the availability of personal data of different patients. This is a crucial requirement for different recommender services. With the increasing trend of service providers to collect a large volume of personal data regarding their end-users, presumably to better serve them. However, a significant part of the data that is typically collected is not essential to the service being offered, or to the completion of the services it was presumably released for. Gathering such unnecessary data can be seen as a privacy threat, and storing it exposes the end-users to further unavoidable risks. In this paper, a privacy enhanced cloud-based recommendation service is proposed for the implicit discovery of appropriate support groups in healthcare social network. A fog based middleware (FMCP) was introduced that runs at patients' sides and allows exchanging of their information to facilities recommending and creating support-groups without disclosing their real preferences to other parties. The membership of patients in various support groups allows receiving highly appropriate and reliable healthcare-related advices. The system utilizes two protocols to attain this goal. Experiments were performed on real dataset.
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