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Research Collection School Of Computing and Information Systems

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Full-Text Articles in Information Security

Efficient Attribute-Based Encryption With Repeated Attributes Optimization, Fawad Khan, Hui Li, Yinghui Zhang, Haider Abbas, Tahreem Yaqoob Jun 2021

Efficient Attribute-Based Encryption With Repeated Attributes Optimization, Fawad Khan, Hui Li, Yinghui Zhang, Haider Abbas, Tahreem Yaqoob

Research Collection School Of Computing and Information Systems

Internet of Things (IoT) is an integration of various technologies to provide technological enhancements. To enforce access control on low power operated battery constrained devices is a challenging issue in IoT scenarios. Attribute-based encryption (ABE) has emerged as an access control mechanism to allow users to encrypt and decrypt data based on an attributes policy. However, to accommodate the expressiveness of policy for practical application scenarios, attributes may be repeated in a policy. For certain policies, the attributes repetition cannot be avoided even after applying the boolean optimization techniques to attain an equivalent smaller length boolean formula. For such policies, …

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Secure Repackage-Proofing Framework For Android Apps Using Collatz Conjecture, Haoyu Ma, Shijia Li, Debin Gao, Chunfu Jia Jun 2021

Secure Repackage-Proofing Framework For Android Apps Using Collatz Conjecture, Haoyu Ma, Shijia Li, Debin Gao, Chunfu Jia

Research Collection School Of Computing and Information Systems

App repackaging has been raising serious concerns about the health of the Android ecosystem, and repackage-proofing is an important mitigation against threat of such attacks. However, existing app repackage-proofing schemes were only evaluated against trivial adversaries simulated using analyzers for other purposes (e.g., disclosing privacy leakage vulnerabilities), hence were shown “effective” mainly because their key programming features were not even supported by those toolkits. Furthermore, existing works have also neglected dynamic adversaries capable of manipulating victim apps at runtime, making them vulnerable against such stronger opponents. In this paper, we propose a novel repackage-proofing framework, which deploys distributed detection and …

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Non-Equivocation In Blockchain: Double-Authentication-Preventing Signatures Gone Contractual, Yannan Li, Willy Susilo, Guomin Yang, Yong Yu, Tran Viet Xuan Phuong, Dongxi Liu Jun 2021

Non-Equivocation In Blockchain: Double-Authentication-Preventing Signatures Gone Contractual, Yannan Li, Willy Susilo, Guomin Yang, Yong Yu, Tran Viet Xuan Phuong, Dongxi Liu

Research Collection School Of Computing and Information Systems

Equivocation is one of the most fundamental problems that need to be solved when designing distributed protocols. Traditional methods to defeat equivocation rely on trusted hardware or particular assumptions, which may hinder their adoption in practice. The advent of blockchain and decentralized cryptocurrencies provides an auspicious breakthrough paradigm to resolve the problem above. In this paper, we propose a blockchain-based solution to address contractual equivocation, which supports user-defined fine-grained policybased equivocation. Specifically, users will be de-incentive if the statements they made breach the predefined access rules. The core of our solution is a newly introduced primitive named Policy-Authentication-Preventing Signature (PoAPS), …

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When Program Analysis Meets Bytecode Search: Targeted And Efficient Inter-Procedural Analysis Of Modern Android Apps In Backdroid, Daoyuan Wu, Debin Gao, Robert H. Deng, Rocky Chang Jun 2021

When Program Analysis Meets Bytecode Search: Targeted And Efficient Inter-Procedural Analysis Of Modern Android Apps In Backdroid, Daoyuan Wu, Debin Gao, Robert H. Deng, Rocky Chang

Research Collection School Of Computing and Information Systems

Widely-used Android static program analysis tools,e.g., Amandroid and FlowDroid, perform the whole-app interprocedural analysis that is comprehensive but fundamentallydifficult to handle modern (large) apps. The average app size hasincreased three to four times over five years. In this paper, weexplore a new paradigm of targeted inter-procedural analysis thatcan skip irrelevant code and focus only on the flows of securitysensitive sink APIs. To this end, we propose a technique calledon-the-fly bytecode search, which searches the disassembled appbytecode text just in time when a caller needs to be located. In thisway, it guides targeted (and backward) inter-procedural analysisstep by step until reaching …

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Expressive Bilateral Access Control For Internet-Of-Things In Cloud-Fog Computing, Shengmin Xu, Jianting Ning, Jinhua Ma, Xinyi Huang, Hwee Hwa Pang, Robert H. Deng Jun 2021

Expressive Bilateral Access Control For Internet-Of-Things In Cloud-Fog Computing, Shengmin Xu, Jianting Ning, Jinhua Ma, Xinyi Huang, Hwee Hwa Pang, Robert H. Deng

Research Collection School Of Computing and Information Systems

As a versatile system architecture, cloud-fog Internet-of-Things (IoT) enables multiple resource-constrained devices to communicate and collaborate with each other. By outsourcing local data and immigrating expensive workloads to cloud service providers and fog nodes (FNs), resource-constrained devices can enjoy data services with low latency and minimal cost. To protect data security and privacy in the untrusted cloud-fog environment, many cryptographic mechanisms have been invented. Unfortunately, most of them are impractical when directly applied to cloud-fog IoT computing, mainly due to the large number of resource-constrained end-devices (EDs). In this paper, we present a secure cloud-fog IoT data sharing system with …

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On Decentralization Of Bitcoin: An Asset Perspective, Ling Cheng, Feida Zhu, Huiwen Liu, Chunyan Miao May 2021

On Decentralization Of Bitcoin: An Asset Perspective, Ling Cheng, Feida Zhu, Huiwen Liu, Chunyan Miao

Research Collection School Of Computing and Information Systems

Since its advent in 2009, Bitcoin, a cryptography-enabled peer-to-peer digital payment system, has been gaining increasing attention from both academia and industry. An effort designed to overcome a cluster of bottlenecks inherent in existing centralized financial systems, Bitcoin has always been championed by the crypto community as an example of the spirit of decentralization. While the decentralized nature of Bitcoin's Proof-of-Work consensus algorithm has often been discussed in great detail, no systematic study has so far been conducted to quantitatively measure the degree of decentralization of Bitcoin from an asset perspective -- How decentralized is Bitcoin as a financial asset? …

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Leveraging Multiple Relations For Fashion Trend Forecasting Based On Social Media, Yujuan Ding, Yunshan Ma, Lizi Liao, Wai Keung Wong, Tat-Seng Chua May 2021

Leveraging Multiple Relations For Fashion Trend Forecasting Based On Social Media, Yujuan Ding, Yunshan Ma, Lizi Liao, Wai Keung Wong, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

—Fashion trend forecasting is of great research significance in providing useful suggestions for both fashion companies and fashion lovers. Although various studies have been devoted to tackling this challenging task, they only studied limited fashion elements with highly seasonal or simple patterns, which could hardly reveal the real complex fashion trends. Moreover, the mainstream solutions for this task are still statistical-based and solely focus on time-series data modeling, which limit the forecast accuracy. Towards insightful fashion trend forecasting, previous work [1] proposed to analyze more fine-grained fashion elements which can informatively reveal fashion trends. Specifically, it focused on detailed fashion …

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A Novel Dynamic Analysis Infrastructure To Instrument Untrusted Execution Flow Across User-Kernel Spaces, Jiaqi Hong, Xuhua Ding May 2021

A Novel Dynamic Analysis Infrastructure To Instrument Untrusted Execution Flow Across User-Kernel Spaces, Jiaqi Hong, Xuhua Ding

Research Collection School Of Computing and Information Systems

Code instrumentation and hardware based event trapping are two primary approaches used in dynamic malware analysis systems. In this paper, we propose a new approach called Execution Flow Instrumentation (EFI) where the analyzer execution flow is interleaved with the target flow in user- and kernel-mode, at junctures flexibly chosen by the analyzer at runtime. We also propose OASIS as the system infrastructure to realize EFI with virtues of the current two approaches, however without their drawbacks. Despite being securely and transparently isolated from the target, the analyzer introspects and controls it in the same native way as instrumentation code. We …

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Fine-Grained And Controllably Redactable Blockchain With Harmful Data Forced Removal, Huiying Hou, Shidi Hao, Jiaming Yuan, Shengmin Xu, Yunlei Zhao May 2021

Fine-Grained And Controllably Redactable Blockchain With Harmful Data Forced Removal, Huiying Hou, Shidi Hao, Jiaming Yuan, Shengmin Xu, Yunlei Zhao

Research Collection School Of Computing and Information Systems

Notoriously, immutability is one of the most striking properties of blockchains. As the data contained in blockchains may be compelled to redact for personal and legal reasons, immutability needs to be skillfully broken. In most existing redactable blockchains, fine-grained redaction and effective deletion of harmful data are mutually exclusive. To close the gap, we propose a fine-grained and controllably redactable blockchain with harmful data forced removal. In the scheme, the originator of the transaction has fine-grained control over who can perform the redaction and which portions of the transaction can be redacted. The redaction transaction is performed after collecting enough …

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When Function Signature Recovery Meets Compiler Optimization, Yan Lin, Debin Gao May 2021

When Function Signature Recovery Meets Compiler Optimization, Yan Lin, Debin Gao

Research Collection School Of Computing and Information Systems

Matching indirect function callees and callers using function signatures recovered from binary executables (number of arguments and argument types) has been proposed to construct a more fine-grained control-flow graph (CFG) to help control-flow integrity (CFI) enforcement. However, various compiler optimizations may violate calling conventions and result in unmatched function signatures. In this paper, we present eight scenarios in which compiler optimizations impact function signature recovery, and report experimental results with 1,344 real-world applications of various optimization levels. Most interestingly, our experiments show that compiler optimizations have both positive and negative impacts on function signature recovery, e.g., its elimination of redundant …

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On The Root Of Trust Identification Problem, Ivan De Oliveira Nunes, Xuhua Ding, Gene Tsudik May 2021

On The Root Of Trust Identification Problem, Ivan De Oliveira Nunes, Xuhua Ding, Gene Tsudik

Research Collection School Of Computing and Information Systems

Trusted Execution Environments (TEEs) are becoming ubiquitous and are currently used in many security applications: from personal IoT gadgets to banking and databases. Prominent examples of such architectures are Intel SGX, ARM TrustZone, and Trusted Platform Modules (TPMs). A typical TEE relies on a dynamic Root of Trust (RoT) to provide security services such as code/data confidentiality and integrity, isolated secure software execution, remote attestation, and sensor auditing. Despite their usefulness, there is currently no secure means to determine whether a given security service or task is being performed by the particular RoT within a specific physical device. We refer …

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Efficient And Verifiable Proof Of Replication With Fast Fault Localization, Haoran Yuan, Xiaofeng Chen, Guowen Xu, Jianting Ning, Joseph K. Liu, Robert H. Deng May 2021

Efficient And Verifiable Proof Of Replication With Fast Fault Localization, Haoran Yuan, Xiaofeng Chen, Guowen Xu, Jianting Ning, Joseph K. Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Proof of replication technique has been widely used to verify whether the cloud service providers (CSPs) store multiple replications of a file with dedicated and unique storage space, which effectively prevents CSPs from colluding and storing only one copy of the file. In this field, many representative schemes have been proposed and applied to various scenarios. However, most of the existing schemes are based on the timing assumption (i.e., the verifier rejects the proof of replication if the prover's response is timeout) and do not explicitly consider the problem of batch verification and fault localization. This will bring unnecessary computational …

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Robust And Universal Seamless Handover Authentication In 5g Hetnets, Yinghui Zhang, Robert H. Deng, Elisa Bertino, Dong Zheng Apr 2021

Robust And Universal Seamless Handover Authentication In 5g Hetnets, Yinghui Zhang, Robert H. Deng, Elisa Bertino, Dong Zheng

Research Collection School Of Computing and Information Systems

The evolving fifth generation (5G) cellular networks will be a collection of heterogeneous and backward-compatible networks. With the increased heterogeneity and densification of 5G heterogeneous networks (HetNets), it is important to ensure security and efficiency of frequent handovers in 5G wireless roaming environments. However, existing handover authentication mechanisms still have challenging issues, such as anonymity, robust traceability and universality. In this paper, we address these issues by introducing RUSH, a Robust and Universal Seamless Handover authentication protocol for 5G HetNets. In RUSH, anonymous mutual authentication with key agreement is enabled for handovers by exploiting the trapdoor collision property of chameleon …

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Looking Back! Using Early Versions Of Android Apps As Attack Vectors, Yue Zhang, Jian Weng, Jia-Si Wneg, Lin Hou, Anjia Yang, Ming Li, Yang Xiang, Deng, Robert H. Apr 2021

Looking Back! Using Early Versions Of Android Apps As Attack Vectors, Yue Zhang, Jian Weng, Jia-Si Wneg, Lin Hou, Anjia Yang, Ming Li, Yang Xiang, Deng, Robert H.

Research Collection School Of Computing and Information Systems

Android platform is gaining explosive popularity. This leads developers to invest resources to maintain the upward trajectory of the demand. Unfortunately, as the profit potential grows higher, the chances of these Apps getting attacked also get higher. Therefore, developers improved the security of their Apps, which limits attackers ability to compromise upgraded versions of the Apps. However, developers cannot enhance the security of earlier versions that have been released on the Play Store. The earlier versions of the App can be subject to reverse engineering and other attacks. In this paper, we find that attackers can use these earlier versions …

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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 …

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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, …

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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 …

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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. …

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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 …

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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 …

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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 …

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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.

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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 …

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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 …

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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 …

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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, …

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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 …

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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 …

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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 …

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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 …

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