Thapar University

About: Thapar University is a education organization based out in Patiāla, Punjab, India. It is known for research contribution in the topics: Computer science & Cloud computing. The organization has 2944 authors who have published 8558 publications receiving 130392 citations.

TCALAS: Temporal Credential-Based Anonymous Lightweight Authentication Scheme for Internet of Drones Environment

Abstract: A user (external party) is interested in accessing the real-time data from some designated drones of a particular fly zone in the Internet of Drones (IoD) deployment. However, to provide this facility, the user needs to be authenticated by an accessed remote drone and vice-versa. After successful authentication both parties can establish a secret session key for the secure communication. To handle this important problem in IoD environment, we design a novel temporal credential based anonymous lightweight user authentication mechanism for IoD environment, called TCALAS. A detailed security analysis using formal security under the broadly applied real-or-random (ROR) model, formal security verification under the broadly used software verification tool, known as automated validation of internet security protocols and applications, and also informal security analysis reveal that TCALAS has the capability to resist various known attacks against passive/active adversary. In addition, a detailed comparative study has been conducted for TCALAS and other related schemes, and the study also reveals that TCALAS provides better security and functionality features, and lower costs in both computation and communication as compared to existing schemes.

A Comprehensive Review on Image Encryption Techniques

Abstract: Image encryption techniques play a significant role in multimedia applications to secure and authenticate digital images. This paper presents a comprehensive study of various image encryption techniques. This paper covers the most significant developments in meta-heuristic based image encryption techniques. The various attacks and performance measures related to image encryption techniques have also been studied. The existing techniques are analyzed with respect to differential, statistical, and key analyses. The main goal of this paper is to give a broad perspective on characteristics of image encryption techniques. The paper concludes by discussing significant advancements in the field of image encryption and highlighting future challenges.

Efficient and Privacy-Preserving Data Aggregation Scheme for Smart Grid Against Internal Adversaries

Abstract: Privacy-preserving data aggregation (P2DA) is an important basic building block that can protect consumer’s privacy in the smart grid environment because it could be used to prevent the extraction of the electricity consumption information of a specific consumer. Due to this important function, the P2DA scheme for the smart grid has attracted a lot of attention from both academic and industry researchers who have proposed many P2DA schemes for the smart grid in recent years. However, most of these P2DA schemes are not secure against internal attackers or cannot provide data integrity. Besides, their computation costs are not satisfactory because the bilinear pairing operation or the hash-to-point operation is performed at the smart meter’s side. To address the deficiencies of previous schemes, we propose a new P2DA scheme against internal attackers using Boneh–Goh–Nissim public key cryptography. The proposed P2DA scheme does not use bilinear pairing or hash-to-point operation making it be more computationally efficient than previous P2DA schemes. We also show that the proposed P2DA scheme is provably secure and can meet various security requirements.

A healthcare monitoring system using random forest and internet of things (IoT)

Abstract: The Internet of Things (IoT) enabled various types of applications in the field of information technology, smart and connected health care is notably a crucial one is one of them. Our physical and mental health information can be used to bring about a positive transformation change in the health care landscape using networked sensors. It makes it possible for monitoring to come to the people who don't have ready access to effective health monitoring system. The captured data can then be analyzed using various machine learning algorithms and then shared through wireless connectivity with medical professionals who can make appropriate recommendations. These scenarios already exist, but we intend to enhance it by analyzing the past data for predicting future problems using prescriptive analytics. It will allow us to move from reactive to visionary approach by rapidly spotting trends and making recommendations on behalf of the actual medical service provider. In this paper, the authors have applied different machine learning techniques and considered public datasets of health care stored in the cloud to build a system, which allows for real time and remote health monitoring built on IoT infrastructure and associated with cloud computing. The system will be allowed to drive recommendations based on the historic and empirical data lying on the cloud. The authors have proposed a framework to uncover knowledge in a database, bringing light to disguise patterns which can help in credible decision making. This paper has evaluated prediction systems for diseases such as heart diseases, breast cancer, diabetes, spect_heart, thyroid, dermatology, liver disorders and surgical data using a number of input attributes related to that particular disease. Experimental results are conducted using a few machine learning algorithms considered in this paper like K-NN, Support Vector Machine, Decision Trees, Random Forest, and MLP.