We revisit the notion of deniability in quantum key exchange (QKE), a topic that remains largely unexplored. In the only work on this subject by Donald Beaver, it is argued that QKE is not necessarily deniable due to an eavesdropping attack that limits key equivocation. We provide more insight into the nature of this attack and how it extends to other constructions such as QKE obtained from uncloneable encryption. We then adopt the framework for quantum authenticated key exchange, developed by , and extend it to introduce the notion of coercer-deniable QKE, formalized in terms of the indistinguishability of real and fake coercer views. Next, we apply results from a recent work by Arrazola and Scarani on covert quantum communication to establish a connection between covert QKE and deniability. We propose DC-QKE, a simple deniable covert QKE protocol, and prove its deniability via a reduction to the security of covert QKE. Finally, we consider how entanglement distillation can be used to enable information-theoretically deniable protocols for QKE and tasks beyond key exchange.
To the best of our knowledge, the only work related to deniability in QKE is a single paper by Beaver @cite_32 , in which the author suggests a negative result arguing that existing QKE schemes are not necessarily deniable.
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We show that claims of perfect security for keys produced by quantum key exchange (QKE) are limited to privacy and integrity. Unlike a one-time pad, QKE does not necessarily enable Sender and Receiver to pretend later to have established a different key. This result is puzzling in light of Mayers' No-Go theorem showing the impossibility of quantum bit commitment. But even though a simple and intuitive application of Mayers' protocol transformation appears sufficient to provide deniability (else QBC would be possible), we show several reasons why such conclusions are ill-founded. Mayers' transformation arguments, while sound for QBC, are insufficient to establish deniability in QKE. Having shed light on several unadvertised pitfalls, we then provide a candidate deniable QKE protocol. This itself indicates further shortfalls in current proof techniques, including reductions that preserve privacy but fail to preserve deniability. In sum, purchasing undeniability with an off-the-shelf QKE protocol is significantly more expensive and dangerous than the mere optic fiber for which perfect security is advertised. We revisit the definitions of zero-knowledge in the Common Reference String (CRS) model and the Random Oracle (RO) model. We argue that even though these definitions syntactically mimic the standard zero-knowledge definition, they loose some of its spirit. In particular, we show that there exist a specific natural security property that is not captured by these definitions. This is the property of deniability. We formally define the notion of deniable zero-knowledge in these models and investigate the possibility of achieving it. Our results are different for the two models: In the wake of recent revelations of mass government surveillance, secure messaging protocols have come under renewed scrutiny. A widespread weakness of existing solutions is the lack of strong deniability properties that allow users to plausibly deny sending messages or participating in conversations if the security of their communications is later compromised. Deniable authenticated key exchanges (DAKEs), the cryptographic protocols responsible for providing deniability in secure messaging applications, cannot currently provide all desirable properties simultaneously. We introduce two new DAKEs with provable security and deniability properties in the Generalized Universal Composability framework. Our primary contribution is the introduction of Spawn, the first non-interactive DAKE that offers forward secrecy and achieves deniability against both offline and online judges; Spawn can be used to improve the deniability properties of the popular TextSecure secure messaging application. We also introduce an interactive dual-receiver cryptosystem that can improve the performance of the only existing interactive DAKE with competitive security properties. To encourage adoption, we implement and evaluate the performance of our schemes while relying solely on standard-model assumptions. We extend the definitional work of Dwork,Naor and Sahai from deniable authentication to deniable key-exchange protocols. We then use these definitions to prove the deniability features of SKEME and SIGMA, two natural and efficient protocols which serve as basis for the Internet Key Exchange (IKE)protocol.SKEME is an encryption-based protocol for which we prove full deniability based on the plaintext awareness of the underlying encryption scheme. Interestingly SKEME's deniability is possibly the first "natural" application which essentially requires plaintext awareness (until now this notion has been mainly used as a tool for proving chosen-ciphertext security).SIGMA, on the other hand,uses non-repudiable signatures for authentication and hence cannot be proven to be fully deniable. Yet we are able to prove a weaker, but meaningful, "partial deniability" property: a party may not be able to deny that it was "alive" at some point in time but can fully deny the contents of its communications and the identity of its interlocutors.We remark that the deniability of SKEME and SIGMA holds in a concurrent setting and does not essentially rely on the random oracle model. Deniable Authentication protocols allow a Sender to authenticate a message for a Receiver, in a way that the Receiver cannot convince a third party that such authentication (or any authentication) ever took place. We present two new approaches to the problem of deniable authentication. The novelty of our schemes is that they do not require the use of CCA-secure encryption (all previous known solutions did), thus showing a different generic approach to the problem of deniable authentication. These new approaches are practically relevant as they lead to more efficient protocols. In the process we point out a subtle definitional issue for deniability. In particular, we propose the notion of forward deniability, which requires that the authentications remain deniable even if the Sender wants to later prove that she authenticated a message. We show that a simulation-based definition of deniability, where the simulation can be computationally indistinguishable from the real protocol does not imply forward deniability. Thus, for deniability one needs to restrict the simulation to be perfect (or statistically close). Our new protocols satisfy this stricter requirement. Traditionally, secure one-round key exchange protocols in the PKI setting have either achieved perfect forward secrecy, or forms of deniability, but not both. On the one hand, achieving perfect forward secrecy against active attackers seems to require some form of authentication of the messages, as in signed Diffie-Hellman style protocols, that subsequently sacrifice deniability. On the other hand, using implicit authentication along the lines of MQV and descendants sacrifices perfect forward secrecy in one round and achieves only weak perfect forward secrecy instead. We show that by reintroducing signatures, it is possible to satisfy both a very strong key-exchange security notion, which we call eCK-PFS, as well as a strong form of deniability, in one-round key exchange protocols. Our security notion for key exchange is stronger than, e.g., the extended-CK model, and captures perfect forward secrecy. Our notion of deniability, which we call peer-and-time deniability, is stronger than that offered by, e.g., the SIGMA protocol. We propose a concrete protocol and prove that it satisfies our definition of key-exchange security in the random oracle model as well as peer-and-time deniability. The protocol combines a signed-Diffie-Hellman message exchange with an MQV-style key computation, and offers a remarkable combination of advanced security properties. Releasing full data records is one of the most challenging problems in data privacy. On the one hand, many of the popular techniques such as data de-identification are problematic because of their dependence on the background knowledge of adversaries. On the other hand, rigorous methods such as the exponential mechanism for differential privacy are often computationally impractical to use for releasing high dimensional data or cannot preserve high utility of original data due to their extensive data perturbation. This paper presents a criterion called plausible deniability that provides a formal privacy guarantee, notably for releasing sensitive datasets: an output record can be released only if a certain amount of input records are indistinguishable, up to a privacy parameter. This notion does not depend on the background knowledge of an adversary. Also, it can efficiently be checked by privacy tests. We present mechanisms to generate synthetic datasets with similar statistical properties to the input data and the same format. We study this technique both theoretically and experimentally. A key theoretical result shows that, with proper randomization, the plausible deniability mechanism generates differentially private synthetic data. We demonstrate the efficiency of this generative technique on a large dataset; it is shown to preserve the utility of original data with respect to various statistical analysis and machine learning measures. Text mining and information retrieval techniques have been developed to assist us with analyzing, organizing and retrieving documents with the help of computers. In many cases, it is desirable that the authors of such documents remain anonymous: Search logs can reveal sensitive details about a user, critical articles or messages about a company or government might have severe or fatal consequences for a critic, and negative feedback in customer surveys might negatively impact business relations if they are identified. Simply removing personally identifying information from a document is, however, insufficient to protect the writer's identity: Given some reference texts of suspect authors, so-called authorship attribution methods can reidentfy the author from the text itself. One of the most prominent models to represent documents in many common text mining and information retrieval tasks is the vector space model where each document is represented as a vector, typically containing its term frequencies or related quantities. We therefore propose an automated text anonymization approach that produces synthetic term frequency vectors for the input documents that can be used in lieu of the original vectors. We evaluate our method on an exemplary text classification task and demonstrate that it only has a low impact on its accuracy. In contrast, we show that our method strongly affects authorship attribution techniques to the level that they become infeasible with a much stronger decline in accuracy. Other than previous authorship obfuscation methods, our approach is the first that fulfills differential privacy and hence comes with a provable plausible deniability guarantee. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. Distributed computer architectures labeled "peer-to-peer" are designed for the sharing of computer resources (content, storage, CPU cycles) by direct exchange, rather than requiring the intermediation or support of a centralized server or authority. Peer-to-peer architectures are characterized by their ability to adapt to failures and accommodate transient populations of nodes while maintaining acceptable connectivity and performance.Content distribution is an important peer-to-peer application on the Internet that has received considerable research attention. Content distribution applications typically allow personal computers to function in a coordinated manner as a distributed storage medium by contributing, searching, and obtaining digital content.In this survey, we propose a framework for analyzing peer-to-peer content distribution technologies. Our approach focuses on nonfunctional characteristics such as security, scalability, performance, fairness, and resource management potential, and examines the way in which these characteristics are reflected in---and affected by---the architectural design decisions adopted by current peer-to-peer systems.We study current peer-to-peer systems and infrastructure technologies in terms of their distributed object location and routing mechanisms, their approach to content replication, caching and migration, their support for encryption, access control, authentication and identity, anonymity, deniability, accountability and reputation, and their use of resource trading and management schemes. Organizations with a large user base, such as Samsung and Google, can potentially benefit from collecting and mining users' data. However, doing so raises privacy concerns, and risks accidental privacy breaches with serious consequences. Local differential privacy (LDP) techniques address this problem by only collecting randomized answers from each user, with guarantees of plausible deniability; meanwhile, the aggregator can still build accurate models and predictors by analyzing large amounts of such randomized data. So far, existing LDP solutions either have severely restricted functionality, or focus mainly on theoretical aspects such as asymptotical bounds rather than practical usability and performance. Motivated by this, we propose Harmony, a practical, accurate and efficient system for collecting and analyzing data from smart device users, while satisfying LDP. Harmony applies to multi-dimensional data containing both numerical and categorical attributes, and supports both basic statistics (e.g., mean and frequency estimates), and complex machine learning tasks (e.g., linear regression, logistic regression and SVM classification). Experiments using real data confirm Harmony's effectiveness. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. Scholars are increasingly citing electronic “web references” which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To “webcite” a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its “instructions for authors” accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) “prospectively” before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted “citing articles” (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues are addressed by honouring respective Internet standards (robot exclusion files, no-cache and no-archive tags). Long-term preservation is ensured by agreements with libraries and digital preservation organizations. The resulting WebCite Index may also have applications for research assessment exercises, being able to measure the impact of Web services and published Web documents through access and Web citation metrics. @PARASPLIT [J Med Internet Res 2005;7(5):e60] A fundamental claim associated with parallel distributed processing (PDP) theories of cognition is that knowledge is coded in a distributed manner in mind and brain. This approach rejects the claim that knowledge is coded in a localist fashion, with words, objects, and simple concepts (e.g. “dog”), that is, coded with their own dedicated representations. One of the putative advantages of this approach is that the theories are biologically plausible. Indeed, advocates of the PDP approach often highlight the close parallels between distributed representations learned in connectionist models and neural coding in brain and often dismiss localist (grandmother cell) theories as biologically implausible. The author reviews a range a data that strongly challenge this claim and shows that localist models provide a better account of single-cell recording studies. The author also contrast local and alternative distributed coding schemes (sparse and coarse coding) and argues that common rejection of grandmother cell theories in neuroscience is due to a misunderstanding about how localist models behave. The author concludes that the localist representations embedded in theories of perception and cognition are consistent with neuroscience; biology only calls into question the distributed representations often learned in PDP models. Multicore architectures, which have multiple processing units on a single chip, are widely viewed as a way to achieve higher processor performance, given that thermal and power problems impose limits on the performance of single-core designs. Accordingly, several chip manufacturers have already released, or will soon release, chips with dual cores, and it is predicted that chips with up to 32 cores will be available within a decade. To effectively use the available processing resources on multicore platforms, software designs should avoid co-executing applications or threads that can worsen the performance of shared caches, if not thrash them. While cache-aware scheduling techniques for such platforms have been proposed for throughput-oriented applications, to the best of our knowledge, no such work has targeted real-time applications. In this paper, we propose and evaluate a cache-aware Pfair-based scheduling scheme for real-time tasks on multicore platforms Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... Using data from computer databases of scientific papers in physics, biomedical research, and computer science, we have constructed networks of collaboration between scientists in each of these disciplines. In these networks two scientists are considered connected if they have coauthored one or more papers together. We have studied many statistical properties of our networks, including numbers of papers written by authors, numbers of authors per paper, numbers of collaborators that scientists have, typical distance through the network from one scientist to another, and a variety of measures of connectedness within a network, such as closeness and betweenness. We further argue that simple networks such as these cannot capture the variation in the strength of collaborative ties and propose a measure of this strength based on the number of papers coauthored by pairs of scientists, and the number of other scientists with whom they worked on those papers. Using a selection of our results, we suggest a variety of possible ways to answer the question, "Who is the best connected scientist?" The popular h-index used to measure scientific output can be described in terms of a pool of evaluated objects (the papers), a quality function on the evaluated objects (the number of citations received by each paper) and a sentencing line crossing the origin, whose intersection with the graph of the quality function yields the index value (in the h-index this is a line with slope 1). Based on this abstraction, we present a new index, the c-index, in which the evaluated objects are the citations received by an author, a group of authors, a journal, etc., the quality function of a citation is the collaboration distance between the authors of the cited and the citing papers, and the sentencing line can take slopes between 0 and ∞. As a result, the new index counts only those citations which are significant enough, where significance is proportional to collaboration distance. Several advantages of the new c-index with respect to previous proposals are discussed. We summarize the currently best known theoretical results for the single-source shortest paths problem for directed graphs with non-negative edge weights. We also point out that a recent result due to Cherkassky, Goldberg and Silverstein (1996) leads to even better time bounds for this problem than claimed by the authors. We propose that the task of question generation should incorporate not only measures of grammaticality, but also a measure of the importance of a question automatically generated. Necessarily, importance of a given question can be judged only in context, so we propose that the data for a shared task be larger than a single sentence, data point or statement in a knowledge base. By focusing on the importance of questions, the community will be in a position to address the shortcomings of existing systems that employ question