Generalized closest substring encryption

We propose a new cryptographic notion called generalized closest substring encryption. In this notion, a ciphertext encrypted with a string S can be decrypted with a private key of another string S′, if there exist a substring of S, i.e. S^, and a substring of S′, i.e. S^′, that are close to each other measured by their overlap distance . The overlap distance between S^ and S^′ is the number of identical positions at which the corresponding symbols are the same. In comparison with other encryption systems, the closest notion is the Fuzzy-IBE proposed by Sahai and Waters. The main difference is that the Fuzzy-IBE measures the overlap distance between S and S′, while ours measures the overlap distance of all of their substrings (including the complete string), and we take the maximum value among those. The overlap distance between their substrings will measure the similarity of S and S′ more precisely compared to the overlap distance between the two complete strings. We note that embedding this overlap distance in an encryption is a challenging task, in particular in order to achieve a practical scheme. Therefore, we invent a new approach to develop a practical generalized closest substring encryption system. The novelty of our approach relies on the way we generate ciphertext and private key representing the complete string so that they can still measure the overlap distance of substrings. The size of ciphertext and private key grow linearly only in the length of the input string. We prove the security in the selective model under a generalization of decision q-Bilinear Diffie-Hellman Exponent assumption

Secrecy and Randomness: Encoding Cloud data Locally using a One-Time Pad

There is no secrecy without randomness, and we address poor cloud security using an analogue chaotic onetime pad encryption system to achieve perfect secrecy. Local encoding returns control to the client and makes stored cloud data unreadable to an adversary. Most cloud service providers encode client data using public encryption algorithms, but ultimately businesses and organisations are responsible for encoding data locally before uploading to the Cloud. As recommended by the Cloud Security Alliance, companies employing authentication and local encryption will reduce or eliminate, EU fines for late data breach discoveries when the EU implements the new general data protection regulations in 2018. Companies failing to detect data breaches within a 72-hour limit will be fined up to four percent of their global annual turnover and estimates of several hundred billion euros could be levied in fines based on the present 146 days average EU breach discovery. The proposed localised encryption system is additional to public encryption, and obeying the rules of one-time pad encryption will mean intercepted encrypted data will be meaningless to an adversary. Furthermore, the encoder has no key distribution problem because applications for it are of “one-to-cloud” type

ENABLING TECHNIQUES FOR EXPRESSIVE FLOW FIELD VISUALIZATION AND EXPLORATION

Flow visualization plays an important role in many scientific and engineering disciplines such as climate modeling, turbulent combustion, and automobile design. The most common method for flow visualization is to display integral flow lines such as streamlines computed from particle tracing. Effective streamline visualization should capture flow patterns and display them with appropriate density, so that critical flow information can be visually acquired. In this dissertation, we present several approaches that facilitate expressive flow field visualization and exploration. First, we design a unified information-theoretic framework to model streamline selection and viewpoint selection as symmetric problems. Two interrelated information channels are constructed between a pool of candidate streamlines and a set of sample viewpoints. Based on these information channels, we define streamline information and viewpoint information to select best streamlines and viewpoints, respectively. Second, we present a focus+context framework to magnify small features and reduce occlusion around them while compacting the context region in a full view. This framework parititions the volume into blocks and deforms them to guide streamline repositioning. The desired deformation is formulated into energy terms and achieved by minimizing the energy function. Third, measuring the similarity of integral curves is fundamental to many tasks such as feature detection, pattern querying, streamline clustering and hierarchical exploration. We introduce FlowString that extracts shape invariant features from streamlines to form an alphabet of characters, and encodes each streamline into a string. The similarity of two streamline segments then becomes a specially designed edit distance between two strings. Leveraging the suffix tree, FlowString provides a string-based method for exploratory streamline analysis and visualization. A universal alphabet is learned from multiple data sets to capture basic flow patterns that exist in a variety of flow fields. This allows easy comparison and efficient query across data sets. Fourth, for exploration of vascular data sets, which contain a series of vector fields together with multiple scalar fields, we design a web-based approach for users to investigate the relationship among different properties guided by histograms. The vessel structure is mapped from the 3D volume space to a 2D graph, which allow more efficient interaction and effective visualization on websites. A segmentation scheme is proposed to divide the vessel structure based on a user specified property to further explore the distribution of that property over space

Confidentiality-Preserving Publish/Subscribe: A Survey

Publish/subscribe (pub/sub) is an attractive communication paradigm for large-scale distributed applications running across multiple administrative domains. Pub/sub allows event-based information dissemination based on constraints on the nature of the data rather than on pre-established communication channels. It is a natural fit for deployment in untrusted environments such as public clouds linking applications across multiple sites. However, pub/sub in untrusted environments lead to major confidentiality concerns stemming from the content-centric nature of the communications. This survey classifies and analyzes different approaches to confidentiality preservation for pub/sub, from applications of trust and access control models to novel encryption techniques. It provides an overview of the current challenges posed by confidentiality concerns and points to future research directions in this promising field

Pipeline Task Scheduling with Appication to Network Processors

Chip Multi-Processors (CMPs) are now available in a variety of systems and provide the opportunity for achieving high computational performance by exploiting application-level parallelism. In the communications environment, network processors (NPs), designed around CMP architectures, are generally usable in a pipelined manner. This leads to the need for static scheduling of tasks on processor pipelines. This thesis considers problems associated with determining optimal schedules for such pipelines. A collection of algorithms is presented with their utility determined by the size and other characteristics of the system. The algorithms employ heuristics, dynamic programming and statistical methods to schedule tasks derived from multiple application flows on pipelines with an arbitrary number of stages. Experimental results indicate that while the dynamic programming algorithm obtains the optimal schedules, heuristics and statistical methods obtain schedules within 10% of the optimal, 95% of the time. Examples are given to show the use of these algorithms for general pipeline/algorithm design and for use in the Network Processor environment with typical networking applications

Patterns and Signals of Biology: An Emphasis On The Role of Post Translational Modifications in Proteomes for Function and Evolutionary Progression

After synthesis, a protein is still immature until it has been customized for a specific task. Post-translational modifications (PTMs) are steps in biosynthesis to perform this customization of protein for unique functionalities. PTMs are also important to protein survival because they rapidly enable protein adaptation to environmental stress factors by conformation change. The overarching contribution of this thesis is the construction of a computational profiling framework for the study of biological signals stemming from PTMs associated with stressed proteins. In particular, this work has been developed to predict and detect the biological mechanisms involved in types of stress response with PTMs in mitochondrial (Mt) and non-Mt protein. Before any mechanism can be studied, there must first be some evidence of its existence. This evidence takes the form of signals such as biases of biological actors and types of protein interaction. Our framework has been developed to locate these signals, distilled from “Big Data” resources such as public databases and the the entire PubMed literature corpus. We apply this framework to study the signals to learn about protein stress responses involving PTMs, modification sites (MSs). We developed of this framework, and its approach to analysis, according to three main facets: (1) by statistical evaluation to determine patterns of signal dominance throughout large volumes of data, (2) by signal location to track down the regions where the mechanisms must be found according to the types and numbers of associated actors at relevant regions in protein, and (3) by text mining to determine how these signals have been previously investigated by researchers. The results gained from our framework enable us to uncover the PTM actors, MSs and protein domains which are the major components of particular stress response mechanisms and may play roles in protein malfunction and disease

PrivMail: A Privacy-Preserving Framework for Secure Emails

Emails have improved our workplace efficiency and communication. However, they are often processed unencrypted by mail servers, leaving them open to data breaches on a single service provider. Public-key based solutions for end-to-end secured email, such as Pretty Good Privacy (PGP) and Secure/Multipurpose Internet Mail Extensions (S/MIME), are available but are not widely adopted due to usability obstacles and also hinder processing of encrypted emails. We propose PrivMail, a novel approach to secure emails using secret sharing methods. Our framework utilizes Secure Multi-Party Computation techniques to relay emails through multiple service providers, thereby preventing any of them from accessing the content in plaintext. Additionally, PrivMail supports private server-side email processing similar to IMAP SEARCH, and eliminates the need for cryptographic certificates, resulting in better usability than public-key based solutions. An important aspect of our framework is its capability to enable third-party searches on user emails while maintaining the privacy of both the email and the query used to conduct the search. We integrate PrivMail into the current email infrastructure and provide a Thunderbird plugin to enhance user-friendliness. To evaluate our solution, we benchmarked transfer and search operations using the Enron Email Dataset and demonstrate that PrivMail is an effective solution for enhancing email security

Sublinear Computation Paradigm

This open access book gives an overview of cutting-edge work on a new paradigm called the “sublinear computation paradigm,” which was proposed in the large multiyear academic research project “Foundations of Innovative Algorithms for Big Data.” That project ran from October 2014 to March 2020, in Japan. To handle the unprecedented explosion of big data sets in research, industry, and other areas of society, there is an urgent need to develop novel methods and approaches for big data analysis. To meet this need, innovative changes in algorithm theory for big data are being pursued. For example, polynomial-time algorithms have thus far been regarded as “fast,” but if a quadratic-time algorithm is applied to a petabyte-scale or larger big data set, problems are encountered in terms of computational resources or running time. To deal with this critical computational and algorithmic bottleneck, linear, sublinear, and constant time algorithms are required. The sublinear computation paradigm is proposed here in order to support innovation in the big data era. A foundation of innovative algorithms has been created by developing computational procedures, data structures, and modelling techniques for big data. The project is organized into three teams that focus on sublinear algorithms, sublinear data structures, and sublinear modelling. The work has provided high-level academic research results of strong computational and algorithmic interest, which are presented in this book. The book consists of five parts: Part I, which consists of a single chapter on the concept of the sublinear computation paradigm; Parts II, III, and IV review results on sublinear algorithms, sublinear data structures, and sublinear modelling, respectively; Part V presents application results. The information presented here will inspire the researchers who work in the field of modern algorithms