Biblio

Network resilience is crucial to ensure reliable and secure operation of critical infrastructures. Although graph theoretic methods have been developed to quantify the topological resilience of networks, i.e., measuring resilience with respect to connectivity, in this study we propose to use the tools from Topological Data Analysis (TDA), Algebraic Topology, and Optimal Transport (OT). In our prior work, we used these tools to create a resilience metric that bypassed the need to embed a network onto a space. We also hypothesized that embeddings could encode different information about a network and that different embeddings could result in different outcomes when computing resilience. In this paper we attempt to test this hypothesis. We will utilize the WEGL framework to compute the embedding for the considered network and compare the results against our prior work, which did not use an embedding process. To our knowledge, this is the first attempt to study the ramifications of choosing an embedding, thus providing a novel understanding into how to choose an embedding and whether such a choice matters when quantifying resilience.

Expressing Keystroke Dynamics (KD) in form of sound opens new avenues to apply sound analysis techniques on KD. However this mapping is not straight-forward as varied feature space, differences in magnitudes of features and human interpretability of the music bring in complexities. We present a musical interface to KD by mapping keystroke features to music features. Music elements like melody, harmony, rhythm, pitch and tempo are varied with respect to the magnitude of their corresponding keystroke features. A pitch embedding technique makes the music discernible among users. Using the data from 30 users, who typed fixed strings multiple times on a desktop, shows that these auditory signals are distinguishable between users by both standard classifiers (SVM, Random Forests and Naive Bayes) and humans alike.

Database outsourcing is one of the important utilities in cloud computing in which the Information Proprietor (IP) transfers the database administration to the Cloud Service Provider (CSP) in order to minimize the administration cost and preservation expenses of the database. Inspite of its immense profit, it undergoes few security issues such as privacy of deployed database and provability of search results. In the recent past, few of the studies have been carried out on provability of search results of Outsourced Database (ODB) that affords correctness and completeness of search results. But in the existing schemes, since there is flow of data between the Information Proprietor and the clients frequently, huge communication cost prevails at the Information Proprietor side. To address this challenge, in this paper we propose Efficient Auditing for Outsourced Database with Token Enforced Cloud Storage (EAODBT). The proposed scheme reduces the large communication cost prevailing at the Information Proprietor side and achieves correctness and completeness of search results even if the mischievous CSP knowingly sends a null set. Experimental analysis show that the proposed scheme has totally reduced the huge communication cost prevailing between Information Proprietor and clients, and simultaneously achieves the correctness and completeness of search results.