Homomorphic Encryption and the Approximate GCD Problem

With the advent of cloud computing, everyone from Fortune 500 businesses to personal consumers to the US government is storing massive amounts of sensitive data in service centers that may not be trustworthy. It is of vital importance to leverage the benefits of storing data in the cloud while simultaneously ensuring the privacy of the data. Homomorphic encryption allows one to securely delegate the processing of private data. As such, it has managed to hit the sweet spot of academic interest and industry demand. Though the concept was proposed in the 1970s, no cryptosystem realizing this goal existed until Craig Gentry published his PhD thesis in 2009. In this thesis, we conduct a study of the two main methods for construction of homomorphic encryption schemes along with functional encryption and the hard problems upon which their security is based. These hard problems include the Approximate GCD problem (A-GCD), the Learning With Errors problem (LWE), and various lattice problems. In addition, we discuss many of the proposed and in some cases implemented practical applications of these cryptosystems. Finally, we focus on the Approximate GCD problem (A-GCD). This problem forms the basis for the security of Gentry\u27s original cryptosystem but has not yet been linked to more standard cryptographic primitives. After presenting several algorithms in the literature that attempt to solve the problem, we introduce some new algorithms to attack the problem

Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learning

In cases of serious crime, including sexual abuse, often the only available information with demonstrated potential for identification is images of the hands. Since this evidence is captured in uncontrolled situations, it is difficult to analyse. As global approaches to feature comparison are limited in this case, it is important to extend to consider local information. In this work, we propose hand-based person identification by learning both global and local deep feature representation. Our proposed method, Global and Part-Aware Network (GPA-Net), creates global and local branches on the conv-layer for learning robust discriminative global and part-level features. For learning the local (part-level) features, we perform uniform partitioning on the conv-layer in both horizontal and vertical directions. We retrieve the parts by conducting a soft partition without explicitly partitioning the images or requiring external cues such as pose estimation. We make extensive evaluations on two large multi-ethnic and publicly available hand datasets, demonstrating that our proposed method significantly outperforms competing approaches

Virtualization Support for Dynamic Core Library Update

International audienceDynamically updating language runtime and core libraries such as collections and threading is challenging since the update mechanism uses such libraries at the same time that it modifies them. To tackle this challenge, we present Dynamic Core Library Update (DCU) as an extension of Dynamic Software Update (DSU) and our approach based on a virtualization architecture. Our solution supports the update of core libraries as any other normal library, avoiding the circular dependencies between the updater and the core libraries. Our benchmarks show that there is no evident performance overhead in comparison with a default execution. Finally, we show that our approach can be applied to real life scenario by introducing a critical update inside a web application with 20 simulated concurrent users. Acknowledgments We thank the European Smalltalk User Group for their support (www.esug.org)

Distributed sensor architecture for intelligent control that supports quality of control and quality of service

This paper is part of a study of intelligent architectures for distributed control and communications systems. The study focuses on optimizing control systems by evaluating the performance of middleware through quality of service (QoS) parameters and the optimization of control using Quality of Control (QoC) parameters. The main aim of this work is to study, design, develop, and evaluate a distributed control architecture based on the Data-Distribution Service for Real-Time Systems (DDS) communication standard as proposed by the Object Management Group (OMG). As a result of the study, an architecture called Frame-Sensor-Adapter to Control (FSACtrl) has been developed. FSACtrl provides a model to implement an intelligent distributed Event-Based Control (EBC) system with support to measure QoS and QoC parameters. The novelty consists of using, simultaneously, the measured QoS and QoC parameters to make decisions about the control action with a new method called Event Based Quality Integral Cycle. To validate the architecture, the first five Braitenberg vehicles have been implemented using the FSACtrl architecture. The experimental outcomes, demonstrate the convenience of using jointly QoS and QoC parameters in distributed control systems.The study described in this paper is a part of the coordinated project COBAMI: Mission-based Hierarchical Control. Education and Science Department Spanish Government. CICYT: MICINN: DPI2011-28507-C02-01/02 and project "Real time distributed control systems" of the Support Program for Research and Development 2012 UPV (PAID-06-12).Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE.; Simarro Fernández, R.; Benet Gilabert, G. (2015). Distributed sensor architecture for intelligent control that supports quality of control and quality of service. Sensors. 15(3):4700-4733. https://doi.org/10.3390/s150304700S4700473315

A browser for incremental programming

2003 European Smalltalk Users Group (ESUG) Conference

A Browser for Incremental Programming

Much of the elegance and power of Smalltalk comes from its programming environment and tools. First introduced more than 20 years ago, the Smalltalk browser enables programmers to "home in" on particular methods using a hierarchy of manually-defined classifications. By its nature, this classification scheme says a lot about the desired state of the code, but little about the actual state of the code as it is being developed. We have extended the Smalltalk browser with dynamically computed virtual categories that dramatically improve the browser's support for incremental programming. We illustrate these improvements by example, and describe the algorithms used to compute the virtual categories efficiently