3,329 research outputs found

    Privacy-Preserving Regular Expression Evaluation on Encrypted Data

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    Motivated by the need to outsource file storage to untrusted clouds while still permitting controlled use of that data by authorized third parties, in this dissertation we present a family of protocols by which a client can evaluate a regular expression on an encrypted file stored at a server (the cloud), once authorized to do so by the file owner. We present a protocol that provably protects the privacy of the regular expression and the file contents from a malicious server and the privacy of the file contents (except for the evaluation result) from an honest-but-curious client. We then extend this protocol in two primary directions. In one direction, we develop a strengthened protocol that enables the client to detect any misbehavior of the server; in particular, the client can verify that the result of its regular-expression evaluation is based on the authentic file stored there by the data owner, and in this sense the file and evaluation result are authenticated to the client. The second direction in which we extend our initial protocol is motivated by the vast adoption of resource-constrained mobile devices, and the fact that our protocols involve relatively intensive client-server interaction and computation on the searching client. We therefore investigate an alternative in which the client (e.g., via her mobile device) can submit her encrypted regular expression to a partially trusted proxy, which then interacts with the server hosting the encrypted data and reports the encrypted evaluation result to the client. Neither the search query nor the result is revealed to an honest-but-curious proxy or malicious server during the process. We demonstrate the practicality of the protocol by prototyping a system to perform regular-expression searches on encrypted emails and evaluate its performance using a real-world email dataset.Doctor of Philosoph

    A Practical Framework for Storing and Searching Encrypted Data on Cloud Storage

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    Security has become a significant concern with the increased popularity of cloud storage services. It comes with the vulnerability of being accessed by third parties. Security is one of the major hurdles in the cloud server for the user when the user data that reside in local storage is outsourced to the cloud. It has given rise to security concerns involved in data confidentiality even after the deletion of data from cloud storage. Though, it raises a serious problem when the encrypted data needs to be shared with more people than the data owner initially designated. However, searching on encrypted data is a fundamental issue in cloud storage. The method of searching over encrypted data represents a significant challenge in the cloud. Searchable encryption allows a cloud server to conduct a search over encrypted data on behalf of the data users without learning the underlying plaintexts. While many academic SE schemes show provable security, they usually expose some query information, making them less practical, weak in usability, and challenging to deploy. Also, sharing encrypted data with other authorized users must provide each document's secret key. However, this way has many limitations due to the difficulty of key management and distribution. We have designed the system using the existing cryptographic approaches, ensuring the search on encrypted data over the cloud. The primary focus of our proposed model is to ensure user privacy and security through a less computationally intensive, user-friendly system with a trusted third party entity. To demonstrate our proposed model, we have implemented a web application called CryptoSearch as an overlay system on top of a well-known cloud storage domain. It exhibits secure search on encrypted data with no compromise to the user-friendliness and the scheme's functional performance in real-world applications.Comment: 146 Pages, Master's Thesis, 6 Chapters, 96 Figures, 11 Table

    PrivMail: A Privacy-Preserving Framework for Secure Emails

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    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

    Round and Communication Balanced Protocols for Oblivious Evaluation of Finite State Machines

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    We propose protocols for obliviously evaluating finite-state machines, i.e., the evaluation is shared between the provider of the finite-state machine and the provider of the input string in such a manner that neither party learns the other's input, and the states being visited are hidden from both. For alphabet size Σ|\Sigma|, number of states Q|Q|, and input length nn, previous solutions have either required a number of rounds linear in nn or communication Ω(nΣQlogQ)\Omega(n|\Sigma||Q|\log|Q|). Our solutions require 2 rounds with communication O(n(Σ+QlogQ))O(n(|\Sigma|+|Q|\log|Q|)). We present two different solutions to this problem, a two-party one and a setting with an untrusted but non-colluding helper

    Forensic Methods and Tools for Web Environments

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    abstract: The Web is one of the most exciting and dynamic areas of development in today’s technology. However, with such activity, innovation, and ubiquity have come a set of new challenges for digital forensic examiners, making their jobs even more difficult. For examiners to become as effective with evidence from the Web as they currently are with more traditional evidence, they need (1) methods that guide them to know how to approach this new type of evidence and (2) tools that accommodate web environments’ unique characteristics. In this dissertation, I present my research to alleviate the difficulties forensic examiners currently face with respect to evidence originating from web environments. First, I introduce a framework for web environment forensics, which elaborates on and addresses the key challenges examiners face and outlines a method for how to approach web-based evidence. Next, I describe my work to identify extensions installed on encrypted web thin clients using only a sound understanding of these systems’ inner workings and the metadata of the encrypted files. Finally, I discuss my approach to reconstructing the timeline of events on encrypted web thin clients by using service provider APIs as a proxy for directly analyzing the device. In each of these research areas, I also introduce structured formats that I customized to accommodate the unique features of the evidence sources while also facilitating tool interoperability and information sharing.Dissertation/ThesisDoctoral Dissertation Computer Science 201

    APIC: A method for automated pattern identification and classification

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    Machine Learning (ML) is a transformative technology at the forefront of many modern research endeavours. The technology is generating a tremendous amount of attention from researchers and practitioners, providing new approaches to solving complex classification and regression tasks. While concepts such as Deep Learning have existed for many years, the computational power for realising the utility of these algorithms in real-world applications has only recently become available. This dissertation investigated the efficacy of a novel, general method for deploying ML in a variety of complex tasks, where best feature selection, data-set labelling, model definition and training processes were determined automatically. Models were developed in an iterative fashion, evaluated using both training and validation data sets. The proposed method was evaluated using three distinct case studies, describing complex classification tasks often requiring significant input from human experts. The results achieved demonstrate that the proposed method compares with, and often outperforms, less general, comparable methods designed specifically for each task. Feature selection, data-set annotation, model design and training processes were optimised by the method, where less complex, comparatively accurate classifiers with lower dependency on computational power and human expert intervention were produced. In chapter 4, the proposed method demonstrated improved efficacy over comparable systems, automatically identifying and classifying complex application protocols traversing IP networks. In chapter 5, the proposed method was able to discriminate between normal and anomalous traffic, maintaining accuracy in excess of 99%, while reducing false alarms to a mere 0.08%. Finally, in chapter 6, the proposed method discovered more optimal classifiers than those implemented by comparable methods, with classification scores rivalling those achieved by state-of-the-art systems. The findings of this research concluded that developing a fully automated, general method, exhibiting efficacy in a wide variety of complex classification tasks with minimal expert intervention, was possible. The method and various artefacts produced in each case study of this dissertation are thus significant contributions to the field of ML
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