Distributed Key Generation and Its Applications

Numerous cryptographic applications require a trusted authority to hold a secret. With a plethora of malicious attacks over the Internet, however, it is difficult to establish and maintain such an authority in online systems. Secret-sharing schemes attempt to solve this problem by distributing the required trust to hold and use the secret over multiple servers; however, they still require a trusted {\em dealer} to choose and share the secret, and have problems related to single points of failure and key escrow. A distributed key generation (DKG) scheme overcomes these hurdles by removing the requirement of a dealer in secret sharing. A (threshold) DKG scheme achieves this using a complete distribution of the trust among a number of servers such that any subset of servers of size greater than a given threshold can reveal or use the shared secret, while any smaller subset cannot. In this thesis, we make contributions to DKG in the computational security setting and describe three applications of it. We first define a constant-size commitment scheme for univariate polynomials over finite fields and use it to reduce the size of broadcasts required for DKG protocols in the synchronous communication model by a linear factor. Further, we observe that the existing (synchronous) DKG protocols do not provide a liveness guarantee over the Internet and design the first DKG protocol for use over the Internet. Observing the necessity of long-term stability, we then present proactive security and group modification protocols for our DKG system. We also demonstrate the practicality of our DKG protocol over the Internet by testing our implementation over PlanetLab. For the applications, we use our DKG protocol to define IND-ID-CCA secure distributed private-key generators (PKGs) for three important identity-based encryption (IBE) schemes: Boneh and Franklin's BF-IBE, Sakai and Kasahara's SK-IBE, and Boneh and Boyen's BB1-IBE. These IBE schemes cover all three important IBE frameworks: full-domain-hash IBEs, exponent-inversion IBEs and commutative-blinding IBEs respectively, and our distributed PKG constructions can easily be modified for other IBE schemes in these frameworks. As the second application, we use our distributed PKG for BF-IBE to define an onion routing circuit construction mechanism in the identity-based setting, which solves the scalability problem in single-pass onion routing circuit construction without hampering forward secrecy. As the final application, we use our DKG implementation to design a threshold signature architecture for quorum-based distributed hash tables and use it to define two robust communication protocols in these peer-to-peer systems

Proceedings of the Sixth General Meeting of the International VLBI Service for Geodesy and Astrometry

This volume is the proceedings of the sixth General Meeting of the International VLBI Service for Geodesy and Astrometry (IVS), held in Hobart, Tasmania, Australia, February 7-13, 2010. The contents of this volume also appear on the IVS Web site at http://ivscc.gsfc.nasa.gov/publications/gm2010. The keynote of the sixth GM was the new perspectives of the next generation VLBI system under the theme "VLBI2010: From Vision to Reality". The goal of the meeting was to provide an interesting and informative program for a wide cross-section of IVS members, including station operators, program managers, and analysts. This volume contains 88 papers. All papers were edited by the editors for usage of the English language, form, and minor content-related issues

Construction and assessment of risk models in medicine

This thesis investigates the application of classical and contemporary statistical methods in medical research attempting to bridge the gap between statistics and clinical medicine. The importance of using simple and advanced statistical methods in constructing and assessing risk models in medicine will be demonstrated by empirical studies related to vascular complications: namely abdominal aortic aneurysm and diabetic retinopathy. First, data preprocessing and preliminary statistical analysis are examined and their application is investigated using data on abdominal aortic aneurysm. We illustrate that when dealing with missing data, the co-operation between statisticians and clinicians is necessary. Also, we show advantages and disadvantages of exploratory analysis. Second, we describe and compare classification models for AAA selective screening. Tow logistic regression models are proposed. We also show that it is important to assess the performance of classifiers by cross-validation and bootstrapping. We also examine models that include other definitions of abnormality, weighted classification and multiple class models. Third, we consider the application of graphical models. We look at different types of graphical models that can be used for classification and for identifying the underlying data structure. The use of Naïve Bayes classifier (NBC) is shown and subsequently we illustrate the Occam’s window model selection in a statistical package for Mixed Interactions Modelling (MIM). The EM-algorithm and multiple imputation method are used to deal with inconsistent entries in the dataset. Finally, modelling mixture of Normal components is investigated by graphical modelling and compared with an alternative minimisation procedure. Finally, we examine risk factors of diabetic sight threating retinopathy (STR). We show the complexity of data preparation and preliminary analysis as well as the importance of using the clinicians’ opinion on selecting appropriate variables. Blood pressure measurements have been examined as predictors of STR. The fundamental role of imputation and its influence on the conclusions of the study are demonstrated. From this study, we conclude that the application of statistics in medicine is an optimisation procedure where both the statistical and the clinical validity need to be taken into account. Also, the combination of simple and advanced methods should be used as it provides additional information. Data, software and time limitations should be considered before and during statistical analysis and appropriate modifications might be implemented to avoid compromising the quality of the study. Finally, medical research should be regarded for statisticians and clinicians as part of a learning process