Due to the increasing number of people using smartphones, location-based services (LBS) have recently become more popular and significant. Nevertheless, a user's capacity to monitor their location might be compromised by using LBS. One popular location-based service (LBS) that gives information about POIs within a certain distance is spatial range query; in this study, we provide EPLQ, an efficient and privacypreserving LBS solution for this purpose. We provide the first privacy-preserving spatial range inquiry using a predicate-only encryption scheme for inner product range (IPRE). With IPRE, it is possible to determine, without compromising privacy, if a given position is within a certain circular zone. Additionally, we suggest a privacy-preserving tree index structure in EPLQ to decrease query time. The security aspects of EPLQ have been validated by a comprehensive security audit. Additionally, comprehensive testing is conducted, and the results show that EPLQ excel

To manage the exponential growth of shared data, cloud computing has emerged as a top contender among application platforms. Users should encrypt their data before sharing it in order to keep it safe on the cloud. By using the cloud, customers with limited computer capabilities may access massive amounts of processing power, bandwidth, storage, and even relevant software that can be shared on an as-needed basis, all while paying for what they really need. As the first barrier against unauthorised access to shared information, access management is of the utmost importance. On the one hand, sensitive data includes outsourced computation tasks, which may contain things like business financial records, private research data, and personally identifiable health information. Encrypting sensitive data before outsourcing it is essential for preventing illicit data leaks and assuring end-to-end security. guarantee the security of data stored online and elsewhere. Important exposur

We target the problem of managing the power states of the servers in a Cloud Data Center (CDC) to jointly minimize the electricity consumption and the maintenance costs derived from the variation of power (and consequently of temperature) on the servers’ CPU. More in detail, we consider a set of virtual machines (VMs) and their requirements in terms of CPU and memory across a set of Time Slot (TSs). We then model the consumed electricity by taking into account the VMs processing costs on the servers, the costs for transferring data between the VMs, and the costs for migrating the VMs across the servers. In addition, we employ a material-based fatigue model to compute the maintenance costs needed to repair the CPU, as a consequence of the variation over time of the server power states. After detailing the problem formulation, we design an original algorithm, called Maintenance and Electricity Costs Data Center (MECDC), to solve it. Our results, obtained over several sce