Technology Policy & Ethics: September 2019
Towards Zero-Trust Database Security – Part 1
By Walid Rjaibi and Mohammad Hammoudeh
The rise of external threats, internal threats and data breaches is driving enterprises to implement zero-trust security to better protect their IT assets and reduce risk. While zero-trust security for networks and identity management systems have received a great deal of focus, very little attention has been devoted to zero-trust security for database systems. This is a major issue as database systems are the custodian of enterprises most critical data and are often the primary target of both external and internal attacks. After all, databases contain valuable data such attackers want to steal. In Part One of this series, we explore both the direct and indirect means through which the same data in a database system can be accessed and the challenges they pose to adhering to the basic tenets of zero-trust security. In Part Two, we outline a set of solutions that are most suitable to address these challenges and enable enterprises to implement zero-trust database security without negatively impacting core database tenets such as query performance.
Artificial Intelligence Powered EEG-EMG Electrodes for Assisting the Paralyzed
Sunil Jacob, Center for Robotics, Varun G Menon, Department of Computer Science and Engineering and Saira Joseph, Department of Electronics and Communication Engineering, SCMS School of Engineering and Technology, India
Paralysis is the loss of muscle function in any part of a person’s body. This occurs when the passage of messages between the brain and muscles is hindered. Over the past few years, it is seen that paralysis is more widespread than ever before; a recent survey shows that around the world, 1 out of 111 people are affected by paralysis [1-2]. This raises the need for developing an interface to aid people who suffer from this unfortunate condition [3-4]. Paralysis limits a person from completing even basic chores in life without any assistance. At times, the paralyzed parts become so stiff, it is difficult for the caregivers to provide assistance. Physiotherapy is also not the best option because of its various limitations such as the need for multiple weekly appointments, long sessions and high cost. Most of our previous work had focused on designing of efficient exoskeletons for rehabilitation. But exoskeletons [5-6] introduced additional burdens to patients with their heaviness and complexity to the caregivers. In this article we discuss an efficient and better way by which friends and caregivers can assist the paralyzed to improve their lifestyle. The primary objective of this article is to discuss the working of Artificial Intelligence based Electroencephalograph (EEG) – Electromyogram (EMG) electrodes for Paralyzed (AI-EEP). This technique has been recently investigated by our team at the Center for Robotics laboratory at SCMS School of Engineering and Technology, India. This device will help paralyzed people to move independently by using the recorded movements of a normal person as a reference.
Automated Machine Learning for Future Networks Including 5G
By Shagufta Henna and Alan Davy
It is possible to select a set of potential candidate machine learning (ML) models based on 5G use-case requirements and characteristics of the ML model, however, it is extremely difficult to predict the best model right at the start. This work proposes an automated ML framework called automated 5G machine learning (Auto5GML), which can be integrated with the unified ML architecture, by the International Telecommunication Union (ITU). Based on the potential search space of ML models, the Auto5GML framework selects the best model to be used for a 5G use-case. It evaluates the potential models by inserting data and running them in parallel by using data parallelism or model parallelism. The proposed framework can optimize the learning performance based on the strict use-case requirements.