In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

The early prediction of deterioration could have an important role in supporting healthcare professionals, as an estimated 11% of deaths in hospital follow a failure to promptly recognize and treat deteriorating patients1. To achieve this goal requires predictions of patient risk that are continuously updated and accurate, and delivered at an individual level with sufficient context and enough time to act. Here we develop a deep learning approach for the continuous risk prediction of future deterioration in patients, building on recent work that models adverse events from electronic health records2–17 and using acute kidney injury—a common and potentially life-threatening condition18—as an exemplar. Our model was developed on a large, longitudinal dataset of electronic health records that cover diverse clinical environments, comprising 703,782 adult patients across 172 inpatient and 1,062 outpatient sites. Our model predicts 55.8% of all inpatient episodes of acute kidney injury, and 90.2% of all acute kidney injuries that required subsequent administration of dialysis, with a lead time of up to 48 h and a ratio of 2 false alerts for every true alert. In addition to predicting future acute kidney injury, our model provides confidence assessments and a list of the clinical features that are most salient to each prediction, alongside predicted future trajectories for clinically relevant blood tests9. Although the recognition and prompt treatment of acute kidney injury is known to be challenging, our approach may offer opportunities for identifying patients at risk within a time window that enables early treatment. A deep learning approach that predicts the risk of acute kidney injury may help to identify patients at risk of health deterioration within a time window that enables early treatment.

A clinically applicable approach to continuous prediction of future acute kidney injury

Vehicular ad hoc networks (VANETs) are becoming the most promising research topic in intelligent transportation systems, because they provide information to deliver comfort and safety to both drivers and passengers. However, unique characteristics of VANETs make security, privacy, and trust management challenging issues in VANETs’ design. This survey article starts with the necessary background of VANETs, followed by a brief treatment of main security services, which have been well studied in other fields. We then focus on an in-depth review of anonymous authentication schemes implemented by five pseudonymity mechanisms. Because of the predictable dynamics of vehicles, anonymity is necessary but not sufficient to thwart tracking an attack that aims at the drivers’ location profiles. Thus, several location privacy protection mechanisms based on pseudonymity are elaborated to further protect the vehicles’ privacy and guarantee the quality of location-based services simultaneously. We also give a comprehensive analysis on various trust management models in VANETs. Finally, considering that current and near-future applications in VANETs are evaluated by simulation, we give a much-needed update on the latest mobility and network simulators as well as the integrated simulation platforms. In sum, this paper is carefully positioned to avoid overlap with existing surveys by filling the gaps and reporting the latest advances in VANETs while keeping it self-explained.

A Survey on Recent Advances in Vehicular Network Security, Trust, and Privacy

Multi-key privacy-preserving deep learning in cloud computing

An Efficient Algorithm for Media-based Surveillance System (EAMSuS) in IoT Smart City Framework

Cognitive Computing for Internet of Medical Things

Performance modeling forms an essential process for evaluation of cloud quality. Cloud performance appraisal process and methods widely differ from that of other proven performance related methodologies being used for domains like computer networks, distributed computing and operations systems. Multi-tier Cloud is a scalable system in which many services or tiers can be constructed for all types of applications. The quality of service of a Multi-tier cloud environment is closely associated with several factors like dependability, availability, reliability, security, perform-ability and each of the performance entities directly or indirectly influences the overall functioning of the cloud. There are many models to evaluate cloud performance and quality, but these traditional models are not efficient enough and consider only certain primary parameters in their evaluation. In this paper, a high-level performance analysis model is proposed that can predict the availability of a Multi-tier cloud enviro...

Availability modeling for multi-tier cloud environment

The Internet of Things and Big Data Analytics : Integrated Platforms and Industry Use Cases

The unmanned aerial vehicles (UAV) have great potential to support search tasks in unstructured environments. These are agile, small and lightweight which can incorporate many sensors that are suitable for detecting an object of interest across various terrains. These UAVs or Drones are perfectly suited for reconnaissance and perimeter sweeps. However, these have their limits and are vulnerable when operated alone. The solution to the problem is using a number of these drones to form a swarm. The swarm of drones can cover a larger area in a short period. The drones will be resembling the flocking nature of birds and the hive nature of bees which means there would never be a single drone operating alone. These drones are connected and the main controller i.e. the hive. The Hive is a narrow AI which is a goal-based system. The goals can be sweeping an area or periodic border patrols. The hive monitors the drones, organizes them. The drones communicate in the swarm wirelessly creating a Flying Adhoc Network. For navigation, this network utilizes GPS technology. This project aims to develop an AI-based system that controls these drones and achieve the goal that is assigned to it.

Artificial Intelligence Supervised Swarm UAVs for Reconnaissance

Big data analytics in recent years had developed lightning fast applications that deal with predictive analysis of huge volumes of data in domains of finance, health, weather, travel, marketing and more. Business analysts take their decisions using the statistical analysis of the available data pulled in from social media, user surveys, blogs and internet resources. Customer sentiment has to be taken into account for designing, launching and pricing a product to be inducted into the market and the emotions of the consumers changes and is influenced by several tangible and intangible factors. The possibility of using Big data analytics to present data in a quickly viewable format giving different perspectives of the same data is appreciated in the field of finance and health, where the advent of decision support system is possible in all aspects of their working. Cognitive computing and artificial intelligence are making big data analytical algorithms to think more on their own, leading to come out with Big data agents with their own functionalities. Predictive Analysis for Digital Marketing Using Big Data: Big Data for Predictive Analysis

Balamurugan Balusamy

Papers

Cognitive Computing for Internet of Medical Things

Availability modeling for multi-tier cloud environment

The Internet of Things and Big Data Analytics : Integrated Platforms and Industry Use Cases

Artificial Intelligence Supervised Swarm UAVs for Reconnaissance

Predictive Analysis for Digital Marketing Using Big Data: Big Data for Predictive Analysis