Mohsen Guizani

Bio: Mohsen Guizani is an academic researcher from Qatar University. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 79, co-authored 1110 publications receiving 31282 citations. Previous affiliations of Mohsen Guizani include Jaypee Institute of Information Technology & University College for Women.

Secure data access for wireless body sensor networks

Abstract: Recently, with the support of mobile cloud computing, large number of health-related data collected from various body sensor networks can be managed efficiently. However, it is an important and challenging issue to keep data security and data privacy in cloud-integrated body sensor network (C-BSN). In this paper, we present a novel secure access control mechanism MC-ABE (Mask Certificate-Attribute Based Encryption) for cloud-integrated body sensor networks. A specific signature is designed to mask the plaintext, then the masked data can be securely outsourced to cloud severs. An authorization certificate composing of the signature and related privilege items is constructed that is used to grant privileges to data receivers. To ensure security, a unique value is chosen to mask the certificate for each data receiver. The analysis shows that the proposed scheme has less computation cost and storage cost compared with other popular models.

POKs Based Secure and Energy-Efficient Access Control for Implantable Medical Devices

Abstract: Implantable medical devices (IMDs), such as pacemakers, implanted cardiac defibrillators and neurostimulators are medical devices implanted into patients’ bodies for monitoring physiological signals and performing medical treatments. Many IMDs have built-in wireless communication modules to facilitate data collecting and device reprogramming by external programmers. The wireless communication brings significant conveniences for advanced applications such as real-time and remote monitoring but also introduces the risk of unauthorized wireless access. The absence of effective access control mechanisms exposes patients’ life to cyber attacks. In this paper, we present a lightweight and universally applicable access control system for IMDs. By leveraging Physically Obfuscated Keys (POKs) as the hardware root of trust, provable security is achieved based on standard cryptographic primitives while attaining high energy efficiency. In addition, barrier-free IMD access under emergent situations is realized by utilizing the patient’s biometrical information. We evaluate our proposed scheme through extensive security analysis and a prototype implementation, which demonstrate our work’s superiority on security and energy efficiency.

A secure client-side framework for protecting the privacy of health data stored on the cloud

Abstract: In the past decade, Cloud-Computing emerged as a new computing concept with a distributed nature using virtual network and systems. Many businesses rely on this technology to keep their systems running but concerns are rising about security breaches in cloud computing. Cloud providers (CPs) are taking significant measures to maintain the security and privacy of the data stored on their premises, in order to preserve the customers' trust. Nevertheless, in certain applications, such as medical health records for example, the medical facility is responsible for preserving the privacy of the patients' data. Although the facility can offload the overhead of storing large amounts of data by using cloud storage, relying solely on the security measures taken by the CP might not be sufficient. Any security breach at the CP's premises does not protect the medical facility from being held accountable. This work aims to solve this problem by presenting a secure approach for storing data on the cloud while keeping the customer in control of the security and privacy of their data.

BIETX: A new quality link metric for Static Wireless Multi-hop Networks

Abstract: In this work, we propose a novel quality link metric; Bandwidth adjusted Inverse ETX (BIETX) for Static Wireless Multi-hop Networks (SWMhNs). The proposed metric considers two path selection parameters into account i.e., packet delivery ratio and link capacity. For computing packet delivery ratios in BIETX, the mechanism of Expected Transmission Count (ETX) is adopted. On the other hand, we take two methods of computing link capacity in BIETX. These methods are based upon the size of pair probes; equal size and different size. We also enhance Optimized Link State Routing (OLSR) protocol while using BIETX. A comparative analysis of proposed metric with equal size and different size pair probe; BIETX-1 and BIETX-2, with two existing quality metrics (ETX and Expected Transmission Time (ETT)) in SWMhNs is also a part of this work. From simulation results, we conclude that BIETX-2 outperforms rest of the metrics because of low routing load in ad-hoc probes, and low routing latencies due to enhancements of routing update frequencies and window size in OLSR.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.