The Evolution of Embedding Metadata in Blockchain Transactions
Authors: Tooba Faisal, Nicolas Courtois, Antoaneta Serguieva
Abstract: The use of blockchains is growing every day, and their utility has greatly expanded from sending and receiving crypto-coins to smart-contracts and decentralized autonomous organizations. Modern blockchains underpin a variety of applications: from designing a global identity to improving satellite connectivity. In our research we look at the ability of blockchains to store metadata in an increasing volume of transactions and with evolving focus of utilization. We further show that basic approaches to improving blockchain privacy also rely on embedding metadata. This paper identifies and classifies real-life blockchain transactions embedding metadata of a number of major protocols running essentially over the bitcoin blockchain. The empirical analysis here presents the evolution of metadata utilization in the recent years, and the discussion suggests steps towards preventing criminal use. Metadata are relevant to any blockchain, and our analysis considers primarily bitcoin as a case study. The paper concludes that simultaneously with both expanding legitimate utilization of embedded metadata and expanding blockchain functionality, the applied research on improving anonymity and security must also attempt to protect against blockchain abuse.Comment: 9 pages, 6 figures, 1 table, 2018 International Joint Conference on Neural Networks
Date: 18 Jun 2018
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How to Make Privacy Policies both GDPR-Compliant and Usable
Authors: Karen Renaud, Lynsay A. Shepherd
Abstract: It is important for organisations to ensure that their privacy policies are General Data Protection Regulation (GDPR) compliant, and this has to be done by the May 2018 deadline. However, it is also important for these policies to be designed with the needs of the human recipient in mind. We carried out an investigation to find out how best to achieve this. We commenced by synthesising the GDPR requirements into a checklist-type format. We then derived a list of usability design guidelines for privacy notifications from the research literature. We augmented the recommendations with other findings reported in the research literature, in order to confirm the guidelines. We conclude by providing a usable and GDPR-compliant privacy policy template for the benefit of policy writers.Comment: 8 pages, 3 figures. Accepted in IEEE CyberSA 2018 Proceedings
Date: 18 Jun 2018
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Exploring the Interconnectedness of Cryptocurrencies using Correlation Networks
Authors: Andrew Burnie
Abstract: Correlation networks were used to detect characteristics which, although fixed over time, have an important influence on the evolution of prices over time. Potentially important features were identified using the websites and whitepapers of cryptocurrencies with the largest userbases. These were assessed using two datasets to enhance robustness: one with fourteen cryptocurrencies beginning from 9 November 2017, and a subset with nine cryptocurrencies starting 9 September 2016, both ending 6 March 2018. Separately analysing the subset of cryptocurrencies raised the number of data points from 115 to 537, and improved robustness to changes in relationships over time. Excluding USD Tether, the results showed a positive association between different cryptocurrencies that was statistically significant. Robust, strong positive associations were observed for six cryptocurrencies where one was a fork of the other; Bitcoin / Bitcoin Cash was an exception. There was evidence for the existence of a group of cryptocurrencies particularly associated with Cardano, and a separate group correlated with Ethereum. The data was not consistent with a token's functionality or creation mechanism being the dominant determinants of the evolution of prices over time but did suggest that factors other than speculation contributed to the price.Comment: Conference Paper presented at The Cryptocurrency Research Conference 2018, 24 May 2018, Anglia Ruskin University Lord Ashcroft International Business School Centre for Financial Research, Cambridge, UK
Date: 18 Jun 2018
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Mending Wall: On the Implementation of Censorship in India
Authors: Devashish Gosain, Anshika Agarwal, Sahil Shekhawat, H. B. Acharya, S. Chakravarty
Abstract: This paper presents a study of the Internet infrastructure in India from the point of view of censorship. First, we show that the current state of affairs---where each ISP implements its own content filters (nominally as per a governmental blacklist)---results in dramatic differences in the censorship experienced by customers. In practice, a well-informed Indian citizen can escape censorship through a judicious choice of service provider. We then consider the question of whether India might potentially follow the Chinese model and institute a single, government-controlled filter. This would not be difficult, as the Indian Internet is quite centralized already. A few "key" ASes (approx 1% of Indian ASes) collectively intercept approx 95% of paths to the censored sites we sample in our study, and also to all publicly-visible DNS servers. 5,000 routers spanning these key ASes would suffice to carry out IP or DNS filtering for the entire country; approx 70% of these routers belong to only two private ISPs. If the government is willing to employ more powerful measures, such as an IP Prefix Hijacking attack, any one of several key ASes can censor traffic for nearly all Indian users. Finally, we demonstrate that such federated censorship by India would cause substantial collateral damage to non-Indian ASes whose traffic passes through Indian cyberspace (which do not legally come under Indian jurisdiction at all).Date: 18 Jun 2018
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A Hierarchical Approach to Encrypted Data Packet Classification in Smart Home Gateways
Authors: Xuejiao Chen, Jiahui Yu, Feng Ye, Pan Wang
Abstract: With the pervasive network based services in smart homes, traditional network management cannot guarantee end-user quality-of-experience (QoE) for all applications. End-user QoE must be supported by efficient network quality-of-service (QoS) measurement and efficient network resource allocation. With the software-defined network technology, the core network may be controlled more efficiently by a network service provider. However, end-to-end network QoS can hardly be improved the managing the core network only. In this paper, we propose an encrypted packet classification scheme for smart home gateways to improve end-to-end QoS measurement from the network operator side. Furthermore, other services such as statistical data collecting, billing to service providers, etc., can be provided without compromising end-user privacy nor security of a network. The proposed encrypted packet classification scheme has a two-level hierarchical structure. One is the service level, which is based on applications that have the same network QoS requirements. A faster classification scheme based on deep learning is proposed to achieve real-time classification with high accuracy. The other one is the application level, which is based on fine-grained applications. A non-real-time classifier can be applied to provide high accuracy. Evaluation is conducted on both level classifiers to demonstrate the efficiency and accuracy of the two types of classifiers.Date: 18 Jun 2018
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Detecting Zero-day Controller Hijacking Attacks on the Power-Grid with Enhanced Deep Learning
Authors: Zecheng He, Aswin Raghavan, Sek Chai, Ruby Lee
Abstract: Attacks against the control processor of a power-grid system, especially zero-day attacks, can be catastrophic. Earlier detection of the attacks can prevent further damage. However, detecting zero-day attacks can be challenging because they have no known code and have unknown behavior. In order to address the zero-day attack problem, we propose a data-driven defense by training a temporal deep learning model, using only normal data from legitimate processes that run daily in these power-grid systems, to model the normal behavior of the power-grid controller. Then, we can quickly find malicious codes running on the processor, by estimating deviations from the normal behavior with a statistical test. Experimental results on a real power-grid controller show that we can detect anomalous behavior with over 99.9% accuracy and nearly zero false positives.Date: 18 Jun 2018
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Attack Detection and Isolation for Discrete-Time Nonlinear Systems
Authors: Tianci Yang, Carlos Murguia, Margreta Kuijper, Dragan Nešić
Abstract: We address the problem of attack detection and isolation for a class of discrete-time nonlinear systems under (potentially unbounded) sensor attacks and measurement noise. We consider the case when a subset of sensors is subject to additive false data injection attacks. Using a bank of the observers, each observer leading to an Input-to-State Stable (ISS) estimation error, we propose two algorithms for detecting and isolating sensor attacks. These algorithms make use of the ISS property of the observers to check whether the trajectories of observers are "consistent" with the attack-free trajectories of the system. Simulations results are presented to illustrate the performance of the proposed algorithms.Date: 18 Jun 2018
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Privacy Preserving Analytics on Distributed Medical Data
Authors: Marina Blanton, Ah Reum Kang, Subhadeep Karan, Jaroslaw Zola
Abstract: Objective: To enable privacy-preserving learning of high quality generative and discriminative machine learning models from distributed electronic health records. Methods and Results: We describe general and scalable strategy to build machine learning models in a provably privacy-preserving way. Compared to the standard approaches using, e.g., differential privacy, our method does not require alteration of the input biomedical data, works with completely or partially distributed datasets, and is resilient as long as the majority of the sites participating in data processing are trusted to not collude. We show how the proposed strategy can be applied on distributed medical records to solve the variables assignment problem, the key task in exact feature selection and Bayesian networks learning. Conclusions: Our proposed architecture can be used by health care organizations, spanning providers, insurers, researchers and computational service providers, to build robust and high quality predictive models in cases where distributed data has to be combined without being disclosed, altered or otherwise compromised.Date: 18 Jun 2018
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Property Testing for Differential Privacy
Authors: Anna Gilbert, Audra McMillan
Abstract: We consider the problem of property testing for differential privacy: with black-box access to a purportedly private algorithm, can we verify its privacy guarantees? In particular, we show that any privacy guarantee that can be efficiently verified is also efficiently breakable in the sense that there exist two databases between which we can efficiently distinguish. We give lower bounds on the query complexity of verifying pure differential privacy, approximate differential privacy, random pure differential privacy, and random approximate differential privacy. We also give algorithmic upper bounds. The lower bounds obtained in the work are infeasible for the scale of parameters that are typically considered reasonable in the differential privacy literature, even when we suppose that the verifier has access to an (untrusted) description of the algorithm. A central message of this work is that verifying privacy requires compromise by either the verifier or the algorithm owner. Either the verifier has to be satisfied with a weak privacy guarantee, or the algorithm owner has to compromise on side information or access to the algorithm.Comment: pre-print: comments welcome
Date: 17 Jun 2018
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Mitigating Botnet Attack Using Encapsulated Detection Mechanism (EDM)
Authors: Maxwell Scale Uwadia Osagie, C. I. Okoye, Amenze Joy Osagie
Abstract: Botnet as it is popularly called became fashionable in recent times owing to it embedded force on network servers. Botnet has an exponential growth of about 170, 000 within network server and client infrastructures per day. The networking environment on monthly basis battle over 5 million bots. Nigeria as a country loses above one hundred and twenty five (N125) billion naira to network fraud annually, end users such as Banks and other financial institutions battle daily the botnet threats.Comment: This paper addresses critical area of network
Date: 16 Jun 2018
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