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    Big Data in Healthcare Transformation: A Short Review
    (Ieee, 2022) Ghaleb, Ebrahim A. A.; Dominic, P. D. D.; Muneer, Amged; Almohammedi, Akram A.
    With the rising expense of healthcare and rising health insurance premiums, preventive healthcare and wellness are necessary. Additionally, a new era in medical records in the healthcare industry, digitalization has ushered in a paradigm shift. As a result, the healthcare industry is seeing an increase in data volume., complexity, variety, and timeliness. As healthcare professionals explore ways to reduce costs while enhancing care processes, delivery, and management, big data (BD), appears as a viable option with the potential to alter the sector. This paradigm changes from reactive to proactive healthcare has the potential to result in total cost savings and eventually economic development. However, while the healthcare business leverages the potential of BD, privacy concerns remain a top priority as new threats and vulnerabilities emerge. We describe the state-of-the-art privacy challenges in BD related to the healthcare business in this study.
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    Vehicle Location Privacy Protection Mechanism Based on Location and Velocity
    (Ieee, 2022) Shaleesh, Izdihar Sh; Almohammedi, Akram A.; Mohammad, Naji, I; Muneer, Amged
    Vehicular ad hoc networks (VANETs) are necessary to protect the lives of drivers by providing them with important information about the condition of the road. However, this type of network is vulnerable to eavesdropping due to the wireless medium of the spread of Beacons messages, which makes drivers worry about the possibility of being pursued through the network. Thus, protecting the privacy of vehicles is an urgent matter in VANETS environments. In this paper, a privacy protection system based on changing pseudonyms depending on the speed of the vehicle and its location inside or outside the predefined mix-zones is proposed. The simulation results show that the proposed strategy is an effective strategy to protect the location information of vehicle drivers. The proposed strategy outperforms existing strategies in terms of mean number tracker confusion, continuous tracking period, and maximum entropy.