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Full-Text Articles in Computer Sciences

Online/Offline Traceable Attribute-Based Encryption [In Chinese], Kai Zhang, Jianfeng Ma, Junwei Zhang, Zuobin Ying, Tao Zhang, Ximeng Liu Jan 2018

Online/Offline Traceable Attribute-Based Encryption [In Chinese], Kai Zhang, Jianfeng Ma, Junwei Zhang, Zuobin Ying, Tao Zhang, Ximeng Liu

Research Collection School Of Computing and Information Systems

Attribute-based encryption (ABE), as a public key encryption, can be utilized for fine-grained access control. However, there are two main drawbacks that limit the applications of attribute-based encryption. First, as different users may have the same decryption privileges in ciphertext-policy attribute-based encryption, it is difficult to catch the users who sell their secret keys for financial benefit. Second, the number of resource-consuming exponentiation operations required to encrypt a message in ciphertext-policy attribute-based encryption grows with the complexity of the access policy, which presents a significant challenge for the users who encrypt data on mobile devices. Towards this end, after proposing …

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Social Collaborative Media In Software Development, Didi Surian, David Lo Jan 2018

Social Collaborative Media In Software Development, Didi Surian, David Lo

Research Collection School Of Computing and Information Systems

In this entry, we discuss various collaborative media which are commonly used among software developers. We start by discussing common communication channels developers used. These communication channels are discussed in two groups: public and enterprise-wide media. We then elaborate project management media in coordinating and managing project activities. Finally, we discuss a number of online knowledge resources, i.e., collaborative/individual knowledge resources and social networks.

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Collaboration Patterns In Software Developer Network, Didi Surian, Ee-Peng Lim, David Lo Jan 2018

Collaboration Patterns In Software Developer Network, Didi Surian, Ee-Peng Lim, David Lo

Research Collection School Of Computing and Information Systems

In this entry, we mine collaboration patterns from a large software developer network (Surian et al. 2010). We consider high- and low-level patterns. High-level patterns correspond to various network-level statistics that we observe to hold in this network. Low-level patterns are topological subgraph patterns that are frequently observed among developers collaborating in the network. Mining topological subgraph patterns are difficult as it is an NP-hard problem. To address this issue, we use a combination of frequent subgraph mining and graph matching by leveraging the power law property exhibited by a large collaboration graph. The technique is applicable to any software …

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A Lightweight Policy Preserving Ehr Sharing Scheme In The Cloud, Zuobin Ying, Lu Wei, Qi Li, Ximeng Liu, Jie Cui Jan 2018

A Lightweight Policy Preserving Ehr Sharing Scheme In The Cloud, Zuobin Ying, Lu Wei, Qi Li, Ximeng Liu, Jie Cui

Research Collection School Of Computing and Information Systems

Electronic Health Record (EHR) is a digital health documentary. It contains not only the health-related records but also the personal sensitive information. Therefore, how to reliably share EHR through the cloud is a challenging issue. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising cryptography prototype, which can achieve fine-grained access control as well as one-to-many encryption. In CP-ABE, access policy is attached to the ciphertext, and however, the access policy is not protected, which will also cause some privacy leakage. In this paper, we propose a policy preserving EHR system on the basis of CP-ABE. Specifically, we designed an algorithm, which …

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Consortium Blockchain-Based Sift: Outsourcing Encrypted Feature Extraction In The D2d Network, Xiaoqin Feng, Jianfeng Ma, Tao Feng, Yinbin Miao, Ximeng Liu Jan 2018

Consortium Blockchain-Based Sift: Outsourcing Encrypted Feature Extraction In The D2d Network, Xiaoqin Feng, Jianfeng Ma, Tao Feng, Yinbin Miao, Ximeng Liu

Research Collection School Of Computing and Information Systems

Privacy-preserving outsourcing algorithms for feature extraction not only reduce users' storage and computation overhead but also preserve the image privacy. However, the existing schemes still suffer from deficiencies induced by security, applications, efficiency and storage. To solve the problems, we implement a consortium chain-based outsourcing feature extraction scheme over encrypted images by using the smart contract, distributed autonomous corporation (DAC), sharding technique, and device to device (D2D) communication, which is secure, widely applied, highly efficient, and has less storage overhead. First, the effectiveness, security, and performance of our scheme are analyzed. Then, the efficiency and storage overhead of our scheme …

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Competency Analytics Tool: Analyzing Curriculum Using Course Competencies, Swapna Gottipati, Venky Shankararaman Jan 2018

Competency Analytics Tool: Analyzing Curriculum Using Course Competencies, Swapna Gottipati, Venky Shankararaman

Research Collection School Of Computing and Information Systems

The applications of learning outcomes and competency frameworks have brought better clarity to engineering programs in many universities. Several frameworks have been proposed to integrate outcomes and competencies into course design, delivery and assessment. However, in many cases, competencies are course-specific and their overall impact on the curriculum design is unknown. Such impact analysis is important for analyzing, discovering gaps and improving the curriculum design. Unfortunately, manual analysis is a painstaking process due to large amounts of competencies across the curriculum. In this paper, we propose an automated method to analyze the competencies and discover their impact on the overall …

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User-Friendly Deniable Storage For Mobile Devices, Bing Chang, Yao Cheng, Bo Chen, Fengwei Zhang, Wen-Tao Zhu, Yanju Liu, Zhan Wang Jan 2018

User-Friendly Deniable Storage For Mobile Devices, Bing Chang, Yao Cheng, Bo Chen, Fengwei Zhang, Wen-Tao Zhu, Yanju Liu, Zhan Wang

Research Collection School Of Computing and Information Systems

Mobile devices are prevalently used to process sensitive data, but traditional encryption may not work when an adversary is able to coerce the device owners to disclose the encryption keys. Plausibly Deniable Encryption (PDE) is thus designed to protect sensitive data against this powerful adversary. In this paper, we present MobiPluto, a user-friendly PDE scheme for denying the existence of sensitive data stored on mobile devices. A salient difference between MobiPluto and the existing PDE systems is that any block-based file systems can be deployed on top of it. To further improve usability and deniability of MobiPluto, we introduce a …

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Tinyvisor: An Extensible Secure Framework On Android Platforms, Dong Shen, Zhoujun Li, Xiaojing Su, Jinxin Ma, Deng, Robert H. Jan 2018

Tinyvisor: An Extensible Secure Framework On Android Platforms, Dong Shen, Zhoujun Li, Xiaojing Su, Jinxin Ma, Deng, Robert H.

Research Collection School Of Computing and Information Systems

As the utilization of mobile platform keeps growing, the security issue of mobile platform becomes a serious threat to user privacy. The current security measures mainly focus on the application level and the framework level, with little protection on the kernel. Virtualization technologies have been used in x86 platforms to protect the security of the kernel. With a higher privilege than the guest operating system, the hypervisor can effectively detect and defend against the malicious activity inside the guest kernel. In this paper, we build a hypervisor framework called TinyVisor leveraging the ARM virtualization extensions to protect the guest system …

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Pricing For A Last-Mile Transportation System, Yiwei Chen, Hai Wang Jan 2018

Pricing For A Last-Mile Transportation System, Yiwei Chen, Hai Wang

Research Collection School Of Computing and Information Systems

The Last-Mile Problem refers to the provision of travel service from the nearest public transportation node to a home or other destination. Last-Mile Transportation System (LMTS), which has recently emerged, provide on-demand shared transportation. We consider an LMTS with multiple passenger types—adults, senior citizens, children, and students. The LMTS designer determines the price for the passengers, last-mile service vehicle capacity, and service fleet size (number of vehicles) for each last-mile region to maximize the social welfare generated by the LMTS. The level of last-mile service (in terms of passenger waiting time) is approximated by using a batch arrival, batch service, …

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User-Friendly Deniable Storage For Mobile Devices, Bing Chang, Yao Cheng, Bo Chen, Fengwei Zhang, Wen-Tao Zhu, Yingjiu Li, Zhan. Wang Jan 2018

User-Friendly Deniable Storage For Mobile Devices, Bing Chang, Yao Cheng, Bo Chen, Fengwei Zhang, Wen-Tao Zhu, Yingjiu Li, Zhan. Wang

Research Collection School Of Computing and Information Systems

Mobile devices are prevalently used to process sensitive data, but traditional encryption may not work when an adversary is able to coerce the device owners to disclose the encryption keys. Plausibly Deniable Encryption (PDE) is thus designed to protect sensitive data against this powerful adversary. In this paper, we present MobiPluto, a user-friendly PDE scheme for denying the existence of sensitive data stored on mobile devices. A salient difference between MobiPluto and the existing PDE systems is that any block-based file systems can be deployed on top of it. To further improve usability and deniability of MobiPluto, we introduce a …

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Exact And Heuristic Approaches For The Multi-Agent Orienteering Problem With Capacity Constraints, Wenjie Wang, Hoong Chuin Lau, Shih-Fen Cheng Jan 2018

Exact And Heuristic Approaches For The Multi-Agent Orienteering Problem With Capacity Constraints, Wenjie Wang, Hoong Chuin Lau, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

This paper introduces and addresses a new multiagent variant of the orienteering problem (OP), namely the multi-agent orienteering problem with capacity constraints (MAOPCC). Different from the existing variants of OP, MAOPCC allows a group of visitors to concurrently visit a node but limits the number of visitors simultaneously being served at each node. In this work, we solve MAOPCC in a centralized manner and optimize the total collected rewards of all agents. A branch and bound algorithm is first proposed to find an optimal MAOPCC solution. Since finding an optimal solution for MAOPCC can become intractable as the number of …

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An Iterated Local Search Algorithm For The Team Orienteering Problem With Variable Profits, Aldy Gunawan, Kien Ming Ng, Graham Kendall, Junhan Lai Jan 2018

An Iterated Local Search Algorithm For The Team Orienteering Problem With Variable Profits, Aldy Gunawan, Kien Ming Ng, Graham Kendall, Junhan Lai

Research Collection School Of Computing and Information Systems

The orienteering problem (OP) is a routing problem that has numerous applications in various domains such as logistics and tourism. The objective is to determine a subset of vertices to visit for a vehicle so that the total collected score is maximized and a given time budget is not exceeded. The extensive application of the OP has led to many different variants, including the team orienteering problem (TOP) and the team orienteering problem with time windows. The TOP extends the OP by considering multiple vehicles. In this article, the team orienteering problem with variable profits (TOPVP) is studied. The main …

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Smart Monitoring Via Participatory Ble Relaying, Meeralakshmi Radhakrishnan, Sougata Sen, Archan Misra, Youngki Lee, Rajesh Krishna Balan Jan 2018

Smart Monitoring Via Participatory Ble Relaying, Meeralakshmi Radhakrishnan, Sougata Sen, Archan Misra, Youngki Lee, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

We espouse the vision of a smart object/campus architecture where sensors attached to smart objects use BLE as communication interface, and where smartphones act as opportunistic relays to transfer the data. We explore the feasibility of the vision with real-world Wi-Fi based location traces from our university campus. Our feasibility studies establish that redundancy exists in user movement within the indoor spaces, and that this redundancy can be exploited for collecting sensor data in an opportunistic, yet fair manner. We develop a couple of alternative heuristics that address the BLE energy asymmetry challenge by intelligently duty-cycling the scanning actions of …

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Secure Smart Metering Based On Lora Technology, Yao Cheng, Hendra Saputra, Leng Meng Goh, Yongdong Wu Jan 2018

Secure Smart Metering Based On Lora Technology, Yao Cheng, Hendra Saputra, Leng Meng Goh, Yongdong Wu

Research Collection School Of Computing and Information Systems

Smart metering allows Substation Automation System (SAS) to remotely and timely read smart meters. Despite its advantages, smart metering brings some challenges. a) It introduces cyber attack risks to the metering system, which may lead to user privacy leakage or even the compromise of smart metering systems. b) Although the majority of meters are located within a regional power supply area, some hard-to-reach nodes are geographically far from the clustered area, which account for a big portion of the entire smart metering operation cost. Facing the above challenges, we propose a secure smart metering infrastructure based on LoRa technology which …

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Securing Display Path For Security-Sensitive Applications On Mobile Devices, Jinhua Cui, Yuanyuan Zhang, Zhiping Cai, Anfeng Liu, Yangyang Li Jan 2018

Securing Display Path For Security-Sensitive Applications On Mobile Devices, Jinhua Cui, Yuanyuan Zhang, Zhiping Cai, Anfeng Liu, Yangyang Li

Research Collection School Of Computing and Information Systems

While smart devices based on ARM processor bring us a lot of convenience, they also become an attractive target of cyber-attacks. The threat is exaggerated as commodity OSes usually have a large code base and suffer from various software vulnerabilities. Nowadays, adversaries prefer to steal sensitive data by leaking the content of display output by a security-sensitive application. A promising solution is to exploit the hardware visualization extensions provided by modern ARM processors to construct a secure display path between the applications and the display device. In this work, we present a scheme named SecDisplay for trusted display service, it …

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Efficient And Privacy-Preserving Outsourced Calculation Of Rational Numbers, Ximeng Liu, Kim-Kwang Raymond Choo, Robert H. Deng, Rongxing Lu, Jian Weng Jan 2018

Efficient And Privacy-Preserving Outsourced Calculation Of Rational Numbers, Ximeng Liu, Kim-Kwang Raymond Choo, Robert H. Deng, Rongxing Lu, Jian Weng

Research Collection School Of Computing and Information Systems

In this paper, we propose a framework for efficient and privacy-preserving outsourced calculation of rational numbers, which we refer to as POCR. Using POCR, a user can securely outsource the storing and processing of rational numbers to a cloud server without compromising the security of the (original) data and the computed results. We present the system architecture of POCR and the associated toolkits required in the privacy preserving calculation of integers and rational numbers to ensure that commonly used outsourced operations can be handled on-the-fly. We then prove that the proposed POCR achieves the goal of secure integer and rational …

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Hybrid Privacy-Preserving Clinical Decision Support System In Fog-Cloud Computing, Ximeng Liu, Robert H. Deng, Yang Yang, Ngoc Hieu Tran, Shangping Zhong Jan 2018

Hybrid Privacy-Preserving Clinical Decision Support System In Fog-Cloud Computing, Ximeng Liu, Robert H. Deng, Yang Yang, Ngoc Hieu Tran, Shangping Zhong

Research Collection School Of Computing and Information Systems

In this paper, we propose a framework for hybrid privacy-preserving clinical decision support system in fog cloud computing, called HPCS. In HPCS, a fog server uses a lightweight data mining method to securely monitor patients' health condition in real-time. The newly detected abnormal symptoms can be further sent to the cloud server for high-accuracy prediction in a privacy-preserving way. Specifically, for the fog servers, we design a new secure outsourced inner-product protocol for achieving secure lightweight single-layer neural network. Also, a privacy-preserving piecewise polynomial calculation protocol allows cloud server to securely perform any activation functions in multiple-layer neural network. Moreover, …

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Multi-Target Deep Neural Networks: Theoretical Analysis And Implementation, Zeng Zeng, Nanying Liang, Xulei Yang, Steven C. H. Hoi Jan 2018

Multi-Target Deep Neural Networks: Theoretical Analysis And Implementation, Zeng Zeng, Nanying Liang, Xulei Yang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

In this work, we propose a novel deep neural network referred to as Multi-Target Deep Neural Network (MT-DNN). We theoretically prove that different stable target models with shared learning paths are stable and can achieve optimal solutions respectively. Based on GoogleNet, we design a single model with three different targets, one for classification, one for regression, and one for masks that is composed of 256  ×  256 sub-models. Unlike bounding boxes used in ImageNet, our single model can draw the shapes of target objects, and in the meanwhile, classify the objects and calculate their sizes. We validate our single MT-DNN …

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Smartwatch-Based Early Gesture Detection & Trajectory Tracking For Interactive Gesture-Driven Applications, Tran Huy Vu, Archan Misra, Quentin Roy, Kenny Tsu Wei Choo, Youngki Lee Jan 2018

Smartwatch-Based Early Gesture Detection & Trajectory Tracking For Interactive Gesture-Driven Applications, Tran Huy Vu, Archan Misra, Quentin Roy, Kenny Tsu Wei Choo, Youngki Lee

Research Collection School Of Computing and Information Systems

The paper explores the possibility of using wrist-worn devices (specifically, a smartwatch) to accurately track the hand movement and gestures for a new class of immersive, interactive gesture-driven applications. These interactive applications need two special features: (a) the ability to identify gestures from a continuous stream of sensor data early–i.e., even before the gesture is complete, and (b) the ability to precisely track the hand’s trajectory, even though the underlying inertial sensor data is noisy. We develop a new approach that tackles these requirements by first building a HMM-based gesture recognition framework that does not need an explicit segmentation step, …

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Integrated Reward Scheme And Surge Pricing In A Ride Sourcing Market, Hai Yang, Chaoyi Shao, Hai Wang, Jieping Ye Jan 2018

Integrated Reward Scheme And Surge Pricing In A Ride Sourcing Market, Hai Yang, Chaoyi Shao, Hai Wang, Jieping Ye

Research Collection School Of Computing and Information Systems

Surge pricing is commonly used in on-demand ride-sourcing platforms (e.g., Uber, Lyft and Didi) to dynamically balance demand and supply. However, since the price for ride service cannot be unlimited, there is usually a reasonable or legitimate range of prices in practice. Such a constrained surge pricing strategy fails to balance demand and supply in certain cases, e.g., even adopting the maximum allowed price cannot reduce the demand to an affordable level during peak hours. In addition, the practice of surge pricing is controversial and has stimulated long debate regarding its pros and cons. In this paper, to address the …

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Identifying And Computing The Exact Core-Determining Class, Ye Luo, Hai Wang Jan 2018

Identifying And Computing The Exact Core-Determining Class, Ye Luo, Hai Wang

Research Collection School Of Computing and Information Systems

The indeterministic relations between unobservable events andobserved outcomes in partially identified models can be characterized bya bipartite graph. Given a probability measure on observed outcomes, theset of feasible probability measures on unobservable events can be definedby a set of linear inequality constraints, according to Artstein’s Theorem.This set of inequalities is called the “core-determining class”. However, thenumber of inequalities defined by Artstein’s Theorem is exponentially increasing with the number of unobservable events, and many inequalitiesmay in fact be redundant. In this paper, we show that the “exact coredetermining class”, i.e., the smallest possible core-determining class, canbe characterized by a set of …

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Slade: A Smart Large-Scale Task Decomposer In Crowdsourcing, Yongxin Tong, Lei Chen, Zimu Zhou, H. V. Jagadish, Lidan Shou Jan 2018

Slade: A Smart Large-Scale Task Decomposer In Crowdsourcing, Yongxin Tong, Lei Chen, Zimu Zhou, H. V. Jagadish, Lidan Shou

Research Collection School Of Computing and Information Systems

Crowdsourcing has been shown to be effective in a wide range of applications, and is seeing increasing use. A large-scale crowdsourcing task often consists of thousands or millions of atomic tasks, each of which is usually a simple task such as binary choice or simple voting. To distribute a large-scale crowdsourcing task to limited crowd workers, a common practice is to pack a set of atomic tasks into a task bin and send to a crowd worker in a batch. It is challenging to decompose a large-scale crowdsourcing task and execute batches of atomic tasks, which ensures reliable answers at …

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Dictionary Learning With Structured Noise, Pan Zhou, Cong Fang, Zhouchen Lin, Chao Zhang, Y. Edward Chang Jan 2018

Dictionary Learning With Structured Noise, Pan Zhou, Cong Fang, Zhouchen Lin, Chao Zhang, Y. Edward Chang

Research Collection School Of Computing and Information Systems

Recently, lots of dictionary learning methods have been proposed and successfully applied. However, many of them assume that the noise in data is drawn from Gaussian or Laplacian distribution and therefore they typically adopt the 2 or 1 norm to characterize these two kinds of noise, respectively. Since this assumption is inconsistent with the real cases, the performance of these methods is limited. In this paper, we propose a novel dictionary learning with structured noise (DLSN) method for handling noisy data. We decompose the original data into three parts: clean data, structured noise, and Gaussian noise, and then characterize them …

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Improved Construction For Inner Product Functional Encryption, Qingsong Zhao, Qingkai Zeng, Ximeng Liu Jan 2018

Improved Construction For Inner Product Functional Encryption, Qingsong Zhao, Qingkai Zeng, Ximeng Liu

Research Collection School Of Computing and Information Systems

Functional encryption (FE) is a vast new paradigm for encryption scheme which allows tremendous flexibility in accessing encrypted data. In a FE scheme, a user can learn specific function of encrypted messages by restricted functional key and reveals nothing else about the messages. Besides the standard notion of data privacy in FE, it should protect the privacy of the function itself which is also crucial for practical applications. In this paper, we construct a secret key FE scheme for the inner product functionality using asymmetric bilinear pairing groups of prime order. Compared with the existing similar schemes, our construction reduces …

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Collaborative Fall Detection Using Smartphone And Kinect, Xue Li, Lanshun Nie, Hanchuan Xu, Xianzhi Wang Jan 2018

Collaborative Fall Detection Using Smartphone And Kinect, Xue Li, Lanshun Nie, Hanchuan Xu, Xianzhi Wang

Research Collection School Of Computing and Information Systems

Humanfall detection has attracted broad attentions as sensors and mobile devices are increasingly adopted in real-life scenarios such as smart homes. The complexity of activities in home environments pose severe challenges to the fall detection research with respect to the detection accuracy. We propose a collaborative detection platform that combines two subsystems: a threshold-based fall detection subsystem using mobile phones and a support vector machine (SVM)-based fall detection subsystem using Kinects. Both subsystems have their respective confidence models and the platform detects falls by fusing the data of both subsystems using two methods: the logical rules-based and D-S evidence fusion …

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Simulation-Based Security Of Function-Hiding Inner Product Encryption, Qingsong Zhao, Qingkai Zeng, Ximeng Liu, Huanliang Xu Jan 2018

Simulation-Based Security Of Function-Hiding Inner Product Encryption, Qingsong Zhao, Qingkai Zeng, Ximeng Liu, Huanliang Xu

Research Collection School Of Computing and Information Systems

Functional encryption (FE) [1,2] is a modern type of encryption scheme that extends several previous notions and allows tremendous flexibility in controlling and computing on encrypted data. FE enables an authority to derive constrained decryption keys that are used by a user to obtain specific functions of encrypted messages. Informally, the authority generates a secret key skf for a function f from a master secret key. Then, the user can only learn f(x) from a ciphertext Enc(x) with skf and reveal nothing else about x.

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Utilizing Hypervisor To Enhance Trustzone’S Introspection Capabilities On Non-Secure World, Zhang-Kai Zhang, Zhou-Jun Li, Chun-He Xia, Jin-Xin Ma, Jinhua Cui Jan 2018

Utilizing Hypervisor To Enhance Trustzone’S Introspection Capabilities On Non-Secure World, Zhang-Kai Zhang, Zhou-Jun Li, Chun-He Xia, Jin-Xin Ma, Jinhua Cui

Research Collection School Of Computing and Information Systems

Widely used on the Android phones, the technology of ARM TrustZone divides the hardware resources of Android phones into two worlds:non-secure world and secure world. The Android operating system used by user is running in the non-secure world, while the non-secure world's introspection systems (e.g., KNOX, Hypervisor) that are based on TrustZone are running in the secure world. These introspection systems have the high privilege. They can dynamically check Android kernel integrity and perform memory management of non-secure world instead of Android kernel. But TrustZonecan can not completely introspect the hardware resources (e.g., Cache) of non-secure world because of the …

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Modeling Engagement Of Programming Students Using Unsupervised Machine Learning Technique, Hua Leong Fwa, Lindsay Marshall Jan 2018

Modeling Engagement Of Programming Students Using Unsupervised Machine Learning Technique, Hua Leong Fwa, Lindsay Marshall

Research Collection School Of Computing and Information Systems

Engagement is instrumental to students’ learning and academic achievements. In this study, we model the engagement states of students who are working on programming exercises in an intelligent tutoring system. Head pose, keystrokes and action logs of students automatically captured within the tutoring system are fed into a Hidden Markov Model for inferring the engagement states of students. With the modeling of students’ engagement on a moment by moment basis, intervention measures can be initiated automatically by the system when necessary to optimize the students’ learning. This study is also one of the few studies that bypass the need for …

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Strong Identity-Based Proxy Signature Schemes, Revisited, Weiwei Liu, Yi Mu, Guomin Yang, Yangguang Tian Jan 2018

Strong Identity-Based Proxy Signature Schemes, Revisited, Weiwei Liu, Yi Mu, Guomin Yang, Yangguang Tian

Research Collection School Of Computing and Information Systems

Proxy signature is a useful cryptographic primitive that has been widely used in many applications. It has attracted a lot of attention since it was introduced. There have been lots of works in constructing efficient and secure proxy signature schemes. In this paper, we identify a new attack that has been neglected by many existing proven secure proxy signature schemes. We demonstrate this attack by launching it against an identity-based proxy signature scheme which is proven secure. We then propose one method that can effectively prevent this attack. The weakness in some other proxy signature schemes can also be fixed …

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Anatomy Of Online Hate: Developing A Taxonomy And Machine Learning Models For Identifying And Classifying Hate In Online News Media, Joni Salminen, Hind Almerekhi, Milica Milenkovic, Soon-Gyu Jung, Haewoon Kwak, Haewoon Kwak, Bernard J. Jansen Jan 2018

Anatomy Of Online Hate: Developing A Taxonomy And Machine Learning Models For Identifying And Classifying Hate In Online News Media, Joni Salminen, Hind Almerekhi, Milica Milenkovic, Soon-Gyu Jung, Haewoon Kwak, Haewoon Kwak, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

Online social media platforms generally attempt to mitigate hateful expressions, as these comments can be detrimental to the health of the community. However, automatically identifying hateful comments can be challenging. We manually label 5,143 hateful expressions posted to YouTube and Facebook videos among a dataset of 137,098 comments from an online news media. We then create a granular taxonomy of different types and targets of online hate and train machine learning models to automatically detect and classify the hateful comments in the full dataset. Our contribution is twofold: 1) creating a granular taxonomy for hateful online comments that includes both …

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