Classification and Verification of Online Handwritten Signatures with Time Causal Information Theory Quantifiers

We present a new approach for online handwritten signature classification and verification based on descriptors stemming from Information Theory. The proposal uses the Shannon Entropy, the Statistical Complexity, and the Fisher Information evaluated over the Bandt and Pompe symbolization of the horizontal and vertical coordinates of signatures. These six features are easy and fast to compute, and they are the input to an One-Class Support Vector Machine classifier. The results produced surpass state-of-the-art techniques that employ higher-dimensional feature spaces which often require specialized software and hardware. We assess the consistency of our proposal with respect to the size of the training sample, and we also use it to classify the signatures into meaningful groups.Comment: Submitted to PLOS On

Fully leakage-resilient signatures revisited: Graceful degradation, noisy leakage, and construction in the bounded-retrieval model

We construct new leakage-resilient signature schemes. Our schemes remain unforgeable against an adversary leaking arbitrary (yet bounded) information on the entire state of the signer (sometimes known as fully leakage resilience), including the random coin tosses of the signing algorithm. The main feature of our constructions is that they offer a graceful degradation of security in situations where standard existential unforgeability is impossible

Quantum Lightning Never Strikes the Same State Twice

Public key quantum money can be seen as a version of the quantum no-cloning theorem that holds even when the quantum states can be verified by the adversary. In this work, investigate quantum lightning, a formalization of "collision-free quantum money" defined by Lutomirski et al. [ICS'10], where no-cloning holds even when the adversary herself generates the quantum state to be cloned. We then study quantum money and quantum lightning, showing the following results: - We demonstrate the usefulness of quantum lightning by showing several potential applications, such as generating random strings with a proof of entropy, to completely decentralized cryptocurrency without a block-chain, where transactions is instant and local. - We give win-win results for quantum money/lightning, showing that either signatures/hash functions/commitment schemes meet very strong recently proposed notions of security, or they yield quantum money or lightning. - We construct quantum lightning under the assumed multi-collision resistance of random degree-2 systems of polynomials. - We show that instantiating the quantum money scheme of Aaronson and Christiano [STOC'12] with indistinguishability obfuscation that is secure against quantum computers yields a secure quantum money schem

Authentication of Students and Students’ Work in E-Learning : Report for the Development Bid of Academic Year 2010/11

Global e-learning market is projected to reach $107.3 billion by 2015 according to a new report by The Global Industry Analyst (Analyst 2010). The popularity and growth of the online programmes within the School of Computer Science obviously is in line with this projection. However, also on the rise are students’ dishonesty and cheating in the open and virtual environment of e-learning courses (Shepherd 2008). Institutions offering e-learning programmes are facing the challenges of deterring and detecting these misbehaviours by introducing security mechanisms to the current e-learning platforms. In particular, authenticating that a registered student indeed takes an online assessment, e.g., an exam or a coursework, is essential for the institutions to give the credit to the correct candidate. Authenticating a student is to ensure that a student is indeed who he says he is. Authenticating a student’s work goes one step further to ensure that an authenticated student indeed does the submitted work himself. This report is to investigate and compare current possible techniques and solutions for authenticating distance learning student and/or their work remotely for the elearning programmes. The report also aims to recommend some solutions that fit with UH StudyNet platform.Submitted Versio