Open Access. Powered by Scholars. Published by Universities.®

Information Security Commons™

Open Access. Powered by Scholars. Published by Universities.®

Singapore Management University

Discipline
Keyword
Publication Year
Publication
Publication Type

Articles 331 - 360 of 1102

Full-Text Articles in Information Security

Weakly Supervised Segmentation Via Instance-Aware Propagation, Huang Xin, Qianshu Zhu, Yongtuo Liu, Shengfeng He Apr 2021

Weakly Supervised Segmentation Via Instance-Aware Propagation, Huang Xin, Qianshu Zhu, Yongtuo Liu, Shengfeng He

Research Collection School Of Computing and Information Systems

Peak Response Map (PRM) highlighting the discriminative regions can be extracted from a pre-trained classification network. We can accurately localize instances of each class with the help of these response maps. However, these maps cannot provide reliable information for segmentation even with off-the-shelf object proposals. This is because neither PRM nor the proposals know which regions can be regarded as a complete instance. In this paper, we tackle this problem by proposing an Instance-aware Cue propagation Network (ICN) with a new proposal-matching strategy. In particular, the ICN aims to filter out background distractions and cover the complete instance, while our …

Go to article

Investigating The Adoption Of Hybrid Encrypted Cloud Data Deduplication With Game Theory, Xueqin Liang, Zheng Yan, Robert H. Deng, Qinghu Zheng Mar 2021

Investigating The Adoption Of Hybrid Encrypted Cloud Data Deduplication With Game Theory, Xueqin Liang, Zheng Yan, Robert H. Deng, Qinghu Zheng

Research Collection School Of Computing and Information Systems

Encrypted data deduplication, along with different preferences in data access control, brings the birth of hybrid encrypted cloud data deduplication (H-DEDU for short). However, whether H-DEDU can be successfully deployed in practice has not been seriously investigated. Obviously, the adoption of H-DEDU depends on whether it can bring economic benefits to all stakeholders. But existing economic models of cloud storage fail to support H-DEDU due to complicated interactions among stakeholders. In this article, we establish a formal economic model of H-DEDU by formulating the utilities of all involved stakeholders, i.e., data holders, data owners, and Cloud Storage Providers (CSPs). Then, …

Go to article

Privacy-Preserving Federated Deep Learning With Irregular Users, Guowen Xu, Hongwei Li, Yun Zhang, Shengmin Xu, Jianting Ning, Robert H. Deng Mar 2021

Privacy-Preserving Federated Deep Learning With Irregular Users, Guowen Xu, Hongwei Li, Yun Zhang, Shengmin Xu, Jianting Ning, Robert H. Deng

Research Collection School Of Computing and Information Systems

Federated deep learning has been widely used in various fields. To protect data privacy, many privacy-preserving approaches have also been designed and implemented in various scenarios. However, existing works rarely consider a fundamental issue that the data shared by certain users (called irregular users) may be of low quality. Obviously, in a federated training process, data shared by many irregular users may impair the training accuracy, or worse, lead to the uselessness of the final model. In this paper, we propose PPFDL, a Privacy-Preserving Federated Deep Learning framework with irregular users. In specific, we design a novel solution to reduce …

Go to article

Fast Scene Labeling Via Structural Inference, Huaidong Zhang, Chu Han, Xiaodan Zhang, Yong Du, Xuemiao Xu, Guoqiang Han, Jing Qin, Shengfeng He Mar 2021

Fast Scene Labeling Via Structural Inference, Huaidong Zhang, Chu Han, Xiaodan Zhang, Yong Du, Xuemiao Xu, Guoqiang Han, Jing Qin, Shengfeng He

Research Collection School Of Computing and Information Systems

Scene labeling or parsing aims to assign pixelwise semantic labels for an input image. Existing CNN-based models cannot leverage the label dependencies, while RNN-based models predict labels within the local context. In this paper, we propose a fast LSTM scene labeling network via structural inference. A minimum spanning tree is used to build the image structure for constructing semantic relationships. This structure allows efficient generation of direct parent-child dependencies for arbitrary levels of superpixels, and thus structural relationships can be learned with LSTM. In particular, we propose a bi-directional recurrent network to model the information flow along the parent-child path. …

Go to article

Privacy-Preserving Multi-Keyword Searchable Encryption For Distributed Systems, Xueqiao Liu, Guomin Yang, Willy Susilo, Joseph Tonien, Jian Shen Mar 2021

Privacy-Preserving Multi-Keyword Searchable Encryption For Distributed Systems, Xueqiao Liu, Guomin Yang, Willy Susilo, Joseph Tonien, Jian Shen

Research Collection School Of Computing and Information Systems

As cloud storage has been widely adopted in various applications, how to protect data privacy while allowing efficient data search and retrieval in a distributed environment remains a challenging research problem. Existing searchable encryption schemes are still inadequate on desired functionality and security/privacy perspectives. Specifically, supporting multi-keyword search under the multi-user setting, hiding search pattern and access pattern, and resisting keyword guessing attacks (KGA) are the most challenging tasks. In this article, we present a new searchable encryption scheme that addresses the above problems simultaneously, which makes it practical to be adopted in distributed systems. It not only enables multi-keyword …

Go to article

Traceable Monero: Anonymous Cryptocurrency With Enhanced Accountability, Yannan Li, Guomin Yang, Wily Susilo, Yong Yu, Man Ho Au, Dongxi Liu Mar 2021

Traceable Monero: Anonymous Cryptocurrency With Enhanced Accountability, Yannan Li, Guomin Yang, Wily Susilo, Yong Yu, Man Ho Au, Dongxi Liu

Research Collection School Of Computing and Information Systems

Monero provides a high level of anonymity for both users and their transactions. However, many criminal activities might be committed with the protection of anonymity in cryptocurrency transactions. Thus, user accountability (or traceability) is also important in Monero transactions, which is unfortunately lacking in the current literature. In this paper, we fill this gap by introducing a new cryptocurrency named Traceable Monero to balance the user anonymity and accountability. Our framework relies on a tracing authority, but is optimistic, in that it is only involved when investigations in certain transactions are required. We formalize the system model and security model …

Go to article

Understanding Adversarial Robustness Via Critical Attacking Route, Tianlin Li, Aishan Liu, Xianglong Liu, Yitao Xu, Chongzhi Zhang, Xiaofei Xie Feb 2021

Understanding Adversarial Robustness Via Critical Attacking Route, Tianlin Li, Aishan Liu, Xianglong Liu, Yitao Xu, Chongzhi Zhang, Xiaofei Xie

Research Collection School Of Computing and Information Systems

Deep neural networks (DNNs) are vulnerable to adversarial examples which are generated by inputs with imperceptible perturbations. Understanding adversarial robustness of DNNs has become an important issue, which would for certain result in better practical deep learning applications. To address this issue, we try to explain adversarial robustness for deep models from a new perspective of critical attacking route, which is computed by a gradient-based influence propagation strategy. Similar to rumor spreading in social net-works, we believe that adversarial noises are amplified and propagated through the critical attacking route. By exploiting neurons' influences layer by layer, we compose the critical …

Go to article

Divide And Capture: An Improved Cryptanalysis Of The Encryption Standard Algorithm Rsa, Willy Susilo, Joseph Tonien, Guomin Yang Feb 2021

Divide And Capture: An Improved Cryptanalysis Of The Encryption Standard Algorithm Rsa, Willy Susilo, Joseph Tonien, Guomin Yang

Research Collection School Of Computing and Information Systems

RSA is a well known standard algorithm used by modern computers to encrypt and decrypt messages. In some applications, to save the decryption time, it is desirable to have a short secret key d compared to the modulus N. The first significant attack that breaks RSA with short secret key given by Wiener in 1990 is based on the continued fraction technique and it works with d < 1/4 root 18 N-.(25). A decade later, in 2000, Boneh and Durfee presented an improved attack based on lattice technique which works with d < N-.(292). Until this day, Boneh-Durfee attack remain as the best attack on RSA with short secret key. In this paper, we revisit the continued fraction technique and propose a new attack on RSA. Our main result shows that when d < root t (2 root 2 + 8/3) N-.(75)/root e, where e is the public exponent and t is a chosen parameter, our attack can break the RSA with the running time of O(tlog (N)). Our attack is especially well suited for the case where e is much smaller than N. When e approximate to N, the Boneh-Durfee attack outperforms ours. As a result, we could simultaneously run both attacks, our new attack and the classical Boneh-Durfee attack as a backup.

Go to article

Differential Training: A Generic Framework To Reduce Label Noises For Android Malware Detection, Jiayun Xu, Yingjiu Li, Robert H. Deng Feb 2021

Differential Training: A Generic Framework To Reduce Label Noises For Android Malware Detection, Jiayun Xu, Yingjiu Li, Robert H. Deng

Research Collection School Of Computing and Information Systems

A common problem in machine learning-based malware detection is that training data may contain noisy labels and it is challenging to make the training data noise-free at a large scale. To address this problem, we propose a generic framework to reduce the noise level of training data for the training of any machine learning-based Android malware detection. Our framework makes use of all intermediate states of two identical deep learning classification models during their training with a given noisy training dataset and generate a noise-detection feature vector for each input sample. Our framework then applies a set of outlier detection …

Go to article

Fireeye: Cybersecurity In Action, Singapore Management University Jan 2021

Fireeye: Cybersecurity In Action, Singapore Management University

Perspectives@SMU

FireEye built its success on its ‘Human + AI’ philosophy. But can a cybersecurity firm get ahead of the attackers and predict an attack…on itself?

Go to article

Proxy-Free Privacy-Preserving Task Matching With Efficient Revocation In Crowdsourcing, Jiangang Shu, Kan Yang, Xiaohua Jia, Ximeng Liu, Cong Wang, Robert H. Deng Jan 2021

Proxy-Free Privacy-Preserving Task Matching With Efficient Revocation In Crowdsourcing, Jiangang Shu, Kan Yang, Xiaohua Jia, Ximeng Liu, Cong Wang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Task matching in crowdsourcing has been extensively explored with the increasing popularity of crowdsourcing. However, privacy of tasks and workers is usually ignored in most of exiting solutions. In this paper, we study the problem of privacy-preserving task matching for crowdsourcing with multiple requesters and multiple workers. Instead of utilizing proxy re-encryption, we propose a proxy-free task matching scheme for multi-requester/multi-worker crowdsourcing, which achieves task-worker matching over encrypted data with scalability and non-interaction. We further design two different mechanisms for worker revocation including ServerLocal Revocation (SLR) and Global Revocation (GR), which realize efficient worker revocation with minimal overhead on the …

Go to article

Novel Techniques In Recovering, Embedding, And Enforcing Policies For Control-Flow Integrity, Yan Lin Jan 2021

Novel Techniques In Recovering, Embedding, And Enforcing Policies For Control-Flow Integrity, Yan Lin

Dissertations and Theses Collection (Open Access)

Control-Flow Integrity (CFI) is an attractive security property with which most injected and code-reuse attacks can be defeated, including advanced attacking techniques like Return-Oriented Programming (ROP). CFI extracts a control-flow graph (CFG) for a given program and instruments the program to respect the CFG. Specifically, checks are inserted before indirect branch instructions. Before these instructions are executed during runtime, the checks consult the CFG to ensure that the indirect branch is allowed to reach the intended target. Hence, any sort of controlflow hijacking would be prevented. There are three fundamental components in CFI enforcement. The first component is accurately recovering …

Go to article

An Efficient Privacy Preserving Message Authentication Scheme For Internet-Of-Things, Jiannan Wei, Tran Viet Xuan Phuong, Guomin Yang Jan 2021

An Efficient Privacy Preserving Message Authentication Scheme For Internet-Of-Things, Jiannan Wei, Tran Viet Xuan Phuong, Guomin Yang

Research Collection School Of Computing and Information Systems

As an essential element of the next generation Internet, Internet of Things (IoT) has been undergoing an extensive development in recent years. In addition to the enhancement of peoples daily lives, IoT devices also generate/gather a massive amount of data that could be utilized by machine learning and big data analytics for different applications. Due to the machine-to-machine communication nature of IoT, data security and privacy are crucial issues that must be addressed to prevent different cyber attacks (e.g., impersonation and data pollution/poisoning attacks). Nevertheless, due to the constrained computation power and the diversity of IoT devices, it is a …

Go to article

Privattnet: Predicting Privacy Risks In Images Using Visual Attention, Zhang Chen, Thivya Kandappu, Vigneshwaran Subbaraju Jan 2021

Privattnet: Predicting Privacy Risks In Images Using Visual Attention, Zhang Chen, Thivya Kandappu, Vigneshwaran Subbaraju

Research Collection School Of Computing and Information Systems

Visual privacy concerns associated with image sharing is a critical issue that need to be addressed to enable safe and lawful use of online social platforms. Users of social media platforms often suffer from no guidance in sharing sensitive images in public, and often face with social and legal consequences. Given the recent success of visual attention based deep learning methods in measuring abstract phenomena like image memorability, we are motivated to investigate whether visual attention based methods could be useful in measuring psychophysical phenomena like “privacy sensitivity”. In this paper we propose PrivAttNet – a visual attention based approach, …

Go to article

Can We Trust Your Explanations? Sanity Checks For Interpreters In Android Malware Analysis, Min Fan, Wenying Wei, Xiaofei Xie, Yang Liu, Xiaohong Guan, Ting Liu Jan 2021

Can We Trust Your Explanations? Sanity Checks For Interpreters In Android Malware Analysis, Min Fan, Wenying Wei, Xiaofei Xie, Yang Liu, Xiaohong Guan, Ting Liu

Research Collection School Of Computing and Information Systems

With the rapid growth of Android malware, many machine learning-based malware analysis approaches are proposed to mitigate the severe phenomenon. However, such classifiers are opaque, non-intuitive, and difficult for analysts to understand the inner decision reason. For this reason, a variety of explanation approaches are proposed to interpret predictions by providing important features. Unfortunately, the explanation results obtained in the malware analysis domain cannot achieve a consensus in general, which makes the analysts confused about whether they can trust such results. In this work, we propose principled guidelines to assess the quality of five explanation approaches by designing three critical …

Go to article

Privacy-Preserving Outsourced Clinical Decision Support System In The Cloud, Ximeng Liu, Robert H. Deng, Kim-Kwang Raymond Choo, Yang Yang Jan 2021

Privacy-Preserving Outsourced Clinical Decision Support System In The Cloud, Ximeng Liu, Robert H. Deng, Kim-Kwang Raymond Choo, Yang Yang

Research Collection School Of Computing and Information Systems

In this paper, we propose a privacy-preserving clinical decision support system using Naïve Bayesian (NB) classifier, hereafter referred to as Peneus, designed for the outsourced cloud computing environment. Peneus allows one to use patient health information to train the NB classifier privately, which can then be used to predict a patient's (undiagnosed) disease based on his/her symptoms in a single communication round. Specifically, we design secure Single Instruction Multiple Data (SIMD) integer circuits using the fully homomorphic encryption scheme, which can greatly increase the performance compared with the original secure integer circuit. Then, we present a privacy-preserving historical Personal Health …

Go to article

Enabling Efficient Spatial Keyword Queries On Encrypted Data With Strong Security Guarantees, Xiangyu Wang, Jianfeng Ma, Feng Li, Ximeng Liu, Yinbin Miao, Robert H. Deng Jan 2021

Enabling Efficient Spatial Keyword Queries On Encrypted Data With Strong Security Guarantees, Xiangyu Wang, Jianfeng Ma, Feng Li, Ximeng Liu, Yinbin Miao, Robert H. Deng

Research Collection School Of Computing and Information Systems

Structured Encryption (STE), which allows a server to provide secure search services on encrypted data structures, has been widely investigated in recent years. To meet expressive search requirements in practical applications, a large number of STE constructions have been proposed either on textual keywords or spatial data. However, STE on spatio-textual data, which are widely used in location-based services, has not been fully investigated. In this paper, we formally define the notion of Spatial Keyword Structured Encryption (SKSE) and propose several concrete SKSE constructions with various efficiencysecurity trade-offs. Firstly, we propose a basic construction with linear search complexity, which only …

Go to article

Lightning-Fast And Privacy-Preserving Outsourced Computation In The Cloud, Ximeng Liu, Robert H. Deng, Pengfei Wu, Yang Yang Dec 2020

Lightning-Fast And Privacy-Preserving Outsourced Computation In The Cloud, Ximeng Liu, Robert H. Deng, Pengfei Wu, Yang Yang

Research Collection School Of Computing and Information Systems

In this paper, we propose a framework for lightning-fast privacy-preserving outsourced computation framework in the cloud, which we refer to as LightCom. Using LightCom, a user can securely achieve the outsource data storage and fast, secure data processing in a single cloud server different from the existing multi-server outsourced computation model. Specifically, we first present a general secure computation framework for LightCom under the cloud server equipped with multiple Trusted Processing Units (TPUs), which face the side-channel attack. Under the LightCom, we design two specified fast processing toolkits, which allow the user to achieve the commonly-used secure integer computation and …

Go to article

Driving Cybersecurity Policy Insights From Information On The Internet, Qiu-Hong Wang, Steven Mark Miller, Robert H. Deng Dec 2020

Driving Cybersecurity Policy Insights From Information On The Internet, Qiu-Hong Wang, Steven Mark Miller, Robert H. Deng

Research Collection School Of Computing and Information Systems

Cybersecurity policy analytics quantitatively evaluates the effectiveness of cybersecurity protection measures consisting of both technical and managerial countermeasures and is inherently interdisciplinary work, drawing on the concepts and methods from economics, business, social science, and law.

Go to article

Differential Privacy Protection Over Deep Learning: An Investigation Of Its Impacted Factors, Ying Lin, Ling-Yan Bao, Ze-Minghui Li, Shu-Sheng Si, Chao-Hsien Chu Dec 2020

Differential Privacy Protection Over Deep Learning: An Investigation Of Its Impacted Factors, Ying Lin, Ling-Yan Bao, Ze-Minghui Li, Shu-Sheng Si, Chao-Hsien Chu

Research Collection School Of Computing and Information Systems

Deep learning (DL) has been widely applied to achieve promising results in many fields, but it still exists various privacy concerns and issues. Applying differential privacy (DP) to DL models is an effective way to ensure privacy-preserving training and classification. In this paper, we revisit the DP stochastic gradient descent (DP-SGD) method, which has been used by several algorithms and systems and achieved good privacy protection. However, several factors, such as the sequence of adding noise, the models used etc., may impact its performance with various degrees. We empirically show that adding noise first and clipping second will not only …

Go to article

Walls Have Ears: Eavesdropping User Behaviors Via Graphics-Interrupt-Based Side Channel, Haoyu Ma, Jianwen Tian, Debin Gao, Jia Chunfu Dec 2020

Walls Have Ears: Eavesdropping User Behaviors Via Graphics-Interrupt-Based Side Channel, Haoyu Ma, Jianwen Tian, Debin Gao, Jia Chunfu

Research Collection School Of Computing and Information Systems

Graphics Processing Units (GPUs) are now playing a vital role in many devices and systems including computing devices, data centers, and clouds, making them the next target of side-channel attacks. Unlike those targeting CPUs, existing side-channel attacks on GPUs exploited vulnerabilities exposed by application interfaces like OpenGL and CUDA, which can be easily mitigated with software patches. In this paper, we investigate the lower-level and native interface between GPUs and CPUs, i.e., the graphics interrupts, and evaluate the side channel they expose. Being an intrinsic profile in the communication between a GPU and a CPU, the pattern of graphics interrupts …

Go to article

Secure And Verifiable Inference In Deep Neural Networks, Guowen Xu, Hongwei Li, Hao Ren, Jianfei Sun, Shengmin Xu, Jianting Ning, Haoming Yang, Kan Yang, Robert H. Deng Dec 2020

Secure And Verifiable Inference In Deep Neural Networks, Guowen Xu, Hongwei Li, Hao Ren, Jianfei Sun, Shengmin Xu, Jianting Ning, Haoming Yang, Kan Yang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Outsourced inference service has enormously promoted the popularity of deep learning, and helped users to customize a range of personalized applications. However, it also entails a variety of security and privacy issues brought by untrusted service providers. Particularly, a malicious adversary may violate user privacy during the inference process, or worse, return incorrect results to the client through compromising the integrity of the outsourced model. To address these problems, we propose SecureDL to protect the model’s integrity and user’s privacy in Deep Neural Networks (DNNs) inference process. In SecureDL, we first transform complicated non-linear activation functions of DNNs to low-degree …

Go to article

A Deep Learning Framework Supporting Model Ownership Protection And Traitor Tracing, Guowen Xu, Hongwei Li, Yuan Zhang, Xiaodong Lin, Robert H. Deng, Xuemin (Sherman) Shen Dec 2020

A Deep Learning Framework Supporting Model Ownership Protection And Traitor Tracing, Guowen Xu, Hongwei Li, Yuan Zhang, Xiaodong Lin, Robert H. Deng, Xuemin (Sherman) Shen

Research Collection School Of Computing and Information Systems

Cloud-based deep learning (DL) solutions have been widely used in applications ranging from image recognition to speech recognition. Meanwhile, as commercial software and services, such solutions have raised the need for intellectual property rights protection of the underlying DL models. Watermarking is the mainstream of existing solutions to address this concern, by primarily embedding pre-defined secrets in a model's training process. However, existing efforts almost exclusively focus on detecting whether a target model is pirated, without considering traitor tracing. In this paper, we present SecureMark_DL, which enables a model owner to embed a unique fingerprint for every customer within parameters …

Go to article

A Secure Flexible And Tampering-Resistant Data Sharing System For Vehicular Social Networks, Jianfei Sun, Hu Xiong, Shufan Zhang, Ximeng Liu, Jiaming Yuan, Robert H. Deng Nov 2020

A Secure Flexible And Tampering-Resistant Data Sharing System For Vehicular Social Networks, Jianfei Sun, Hu Xiong, Shufan Zhang, Ximeng Liu, Jiaming Yuan, Robert H. Deng

Research Collection School Of Computing and Information Systems

Vehicular social networks (VSNs) have emerged as the promising paradigm of vehicular networks that can improve traffic safety, relieve traffic congestion and even provide comprehensive social services by sharing vehicular sensory data. To selectively share the sensory data with other vehicles in the vicinity and reduce the local storage burden of vehicles, the vehicular sensory data are usually outsourced to vehicle cloud server for sharing and searching. However, existing data sharing systems for VSNs can neither provide secure selective one-to-many data sharing and verifiable data retrieval over encrypted data nor ensure that the integrity of retrieved data. In this paper, …

Go to article

Multi-User Verifiable Searchable Symmetric Encryption For Cloud Storage, Xueqiao Liu, Guomin Yang, Guomin Yang Nov 2020

Multi-User Verifiable Searchable Symmetric Encryption For Cloud Storage, Xueqiao Liu, Guomin Yang, Guomin Yang

Research Collection School Of Computing and Information Systems

In a cloud data storage system, symmetric key encryption is usually used to encrypt files due to its high efficiency. In order allow the untrusted/semi-trusted cloud storage server to perform searching over encrypted data while maintaining data confidentiality, searchable symmetric encryption (SSE) has been proposed. In a typical SSE scheme, a users stores encrypted files on a cloud storage server and later can retrieve the encrypted files containing specific keywords. The basic security requirement of SSE is that the cloud server learns no information about the files or the keywords during the searching process. Some SSE schemes also offer additional …

Go to article

A Tripartite Model Of Trust In Facebook: Acceptance Of Information Personalization, Privacy Concern, And Privacy Literacy, Sonny Rosenthal, Ole-Christian Wasenden, Gorm-Andreas Gronnevet, Rich Ling Nov 2020

A Tripartite Model Of Trust In Facebook: Acceptance Of Information Personalization, Privacy Concern, And Privacy Literacy, Sonny Rosenthal, Ole-Christian Wasenden, Gorm-Andreas Gronnevet, Rich Ling

Research Collection College of Integrative Studies

This study draws on the mental accounting perspective and a tripartite model of trust to explain why users trust Facebook. We argue that trust in Facebook is related to (1) trust in companies that collect personal data, (2) acceptance of information personalization, (3) low privacy concern, and (4) low privacy literacy. Further, we argue that privacy literacy amplifies the relationship between privacy concern and the other factors. This is because, among individuals with high privacy literacy, privacy concern is especially diagnostic of the potential harms of a loss of privacy. These arguments align broadly with theorizations about factors influencing privacy-related …

Go to article

Coinwatch: A Clone-Based Approach For Detecting Vulnerabilities In Cryptocurrencies, Qingze Hum, Wei Jin Tan, Shi Ying Tey, Latasha Lenus, Ivan Homoliak, Yun Lin, Jun Sun Nov 2020

Coinwatch: A Clone-Based Approach For Detecting Vulnerabilities In Cryptocurrencies, Qingze Hum, Wei Jin Tan, Shi Ying Tey, Latasha Lenus, Ivan Homoliak, Yun Lin, Jun Sun

Research Collection School Of Computing and Information Systems

Cryptocurrencies have become very popular in recent years. Thousands of new cryptocurrencies have emerged, proposing new and novel techniques that improve on Bitcoin's core innovation of the blockchain data structure and consensus mechanism. However, cryptocurrencies are a major target for cyber-attacks, as they can be sold on exchanges anonymously and most cryptocurrencies have their codebases publicly available. One particular issue is the prevalence of code clones in cryptocurrencies, which may amplify security threats. If a vulnerability is found in one cryptocurrency, it might be propagated into other cloned cryptocurrencies. In this work, we propose a systematic remedy to this problem, …

Go to article

Sfuzz: An Efficient Adaptive Fuzzer For Solidity Smart Contracts, Tai D. Nguyen, Long H. Pham, Jun Sun, Yun Lin, Minh Quang Tran Nov 2020

Sfuzz: An Efficient Adaptive Fuzzer For Solidity Smart Contracts, Tai D. Nguyen, Long H. Pham, Jun Sun, Yun Lin, Minh Quang Tran

Research Collection School Of Computing and Information Systems

Smart contracts are Turing-complete programs that execute on the infrastructure of the blockchain, which often manage valuable digital assets. Solidity is one of the most popular programming languages for writing smart contracts on the Ethereum platform. Like traditional programs, smart contracts may contain vulnerabilities. Unlike traditional programs, smart contracts cannot be easily patched once they are deployed. It is thus important that smart contracts are tested thoroughly before deployment. In this work, we present an adaptive fuzzer for smart contracts on the Ethereum platform called sFuzz. Compared to existing Solidity fuzzers, sFuzz combines the strategy in the AFL fuzzer and …

Go to article

Boosting Privately: Federated Extreme Gradient Boosting For Mobile Crowdsensing, Yang Liu, Zhuo Ma, Ximeng Liu, Siqi Ma, Surya Nepal, Robert H. Deng, Kui Ren Nov 2020

Boosting Privately: Federated Extreme Gradient Boosting For Mobile Crowdsensing, Yang Liu, Zhuo Ma, Ximeng Liu, Siqi Ma, Surya Nepal, Robert H. Deng, Kui Ren

Research Collection School Of Computing and Information Systems

Recently, Google and other 24 institutions proposed a series of open challenges towards federated learning (FL), which include application expansion and homomorphic encryption (HE). The former aims to expand the applicable machine learning models of FL. The latter focuses on who holds the secret key when applying HE to FL. For the naive HE scheme, the server is set to master the secret key. Such a setting causes a serious problem that if the server does not conduct aggregation before decryption, a chance is left for the server to access the user’s update. Inspired by the two challenges, we propose …

Go to article

Attribute-Based Keyword Search Over Hierarchical Data In Cloud Computing, Yinbin Miao, Jianfeng Ma, Ximeng Liu, Xinghua Li, Qi Jiang, Junwei Zhang Nov 2020

Attribute-Based Keyword Search Over Hierarchical Data In Cloud Computing, Yinbin Miao, Jianfeng Ma, Ximeng Liu, Xinghua Li, Qi Jiang, Junwei Zhang

Research Collection School Of Computing and Information Systems

Searchable encryption (SE) has been a promising technology which allows users to perform search queries over encrypted data. However, the most of existing SE schemes cannot deal with the shared records that have hierarchical structures. In this paper, we devise a basic cryptographic primitive called as attribute-based keyword search over hierarchical data (ABKS-HD) scheme by using the ciphertext-policy attribute-based encryption (CP-ABE) technique, but this basic scheme cannot satisfy all the desirable requirements of cloud systems. The facts that the single keyword search will yield many irrelevant search results and the revoked users can access the unauthorized data with the old …

Go to article

First Prev Next Last