2017 IEEE Trustcom/BigDataSE/ICESS最新文献_第8页

Toward Proxy Re-encryption From Learning with Errors in the Exponent 从指数误差学习到代理再加密

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.300

Zengpeng Li, Chunguang Ma, Ding Wang, M. Zhao, Qian Zhao, Lu Zhou

{"title":"Toward Proxy Re-encryption From Learning with Errors in the Exponent","authors":"Zengpeng Li, Chunguang Ma, Ding Wang, M. Zhao, Qian Zhao, Lu Zhou","doi":"10.1109/Trustcom/BigDataSE/ICESS.2017.300","DOIUrl":"https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.300","url":null,"abstract":"Proxy re-encryption (PRE) is an important cryptographic primitive used for private information sharing. However, the recent advance in quantum computer has potentially crippled its security, as the traditional decisional Diffie-Hellman (DDH)-based PRE is venerable to the quantum attack. Thus, learning with errors (LWE)-based PRE schemes, as a kind of latticebased construction with the inherent quantum-resistant property, has attracted special research interest. Unfortunately, the main drawback of lattice-based public key encryption scheme is noise management after multiplication evaluation. Many cryptographers have been devoted to controlling the expansion of noise. In this line of work, Dagdelen-Gajek-G¨opfert (DGG) put forth the notion of learning with errors in the exponent (LWEE) which is based on lattice and group-theoretic assumption, meanwhile demonstrated a paradigm for constructing efficient quantum resistance public key schemes. In this paper, on top of DGG, we construct a single-bit, single-hop and unidirectional LWEE- based PRE scheme with indistinguishable chosen plaintext attack (IND-CPA) security. To the best of our knowledge, our scheme is the first LWEE-based PRE scheme.","PeriodicalId":170253,"journal":{"name":"2017 IEEE Trustcom/BigDataSE/ICESS","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114678632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Passive Client-based Approach to Detect Evil Twin Attacks 一种基于被动客户端的恶意双攻击检测方法

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.242

Qian Lu, Haipeng Qu, Y. Zhuang, Xi Jun Lin, Yanyong Zhu, Yunzheng Liu

引用次数: 12

GPU Register Packing: Dynamically Exploiting Narrow-Width Operands to Improve Performance GPU寄存器打包:动态利用窄宽度操作数来提高性能

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.308

Xin Wang, Wei Zhang

{"title":"GPU Register Packing: Dynamically Exploiting Narrow-Width Operands to Improve Performance","authors":"Xin Wang, Wei Zhang","doi":"10.1109/Trustcom/BigDataSE/ICESS.2017.308","DOIUrl":"https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.308","url":null,"abstract":"Graphics processing units(GPUs) have been increasingly used to accelerate general purpose computations. By exploiting massive thread-level parallelism (TLP), GPUs can achieve high throughput as well as memory latency hiding. As a result, a very large register file (RF) is typically required to enable fast and low-cost context switching between tens of thousands of active threads. However, RF resource is still insufficient to enable all thread level parallelism and the lack of RF resources can hurt performance by limiting the occupancy of GPU threads. Moreover, if the available RF capacity can not fit the requirement of a thread block, GPU needs to fetch some variables from local memory which may lead to long memory access latencies. By observing that a large percentage of computed results actually have fewer significant bits compared to the full width of a 32-bit register for many GPGPU applications, we propose a GPU register packing scheme to dynamically exploit narrowwidth operands and pack multiple operands into a single fullwidth register. By using dynamically register packing, more RF space is available which allows GPU to enable more TLP through assigning additional thread blocks on SMs (Streaming Multiprocessors) and thus improve performance. The experimental results show that our GPU register packing scheme can achieve up to 1.96X speedup and 1.18X on average.","PeriodicalId":170253,"journal":{"name":"2017 IEEE Trustcom/BigDataSE/ICESS","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115717953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Robust Vehicle Classification Based on the Combination of Deep Features and Handcrafted Features 基于深度特征和手工特征结合的鲁棒车辆分类

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.323

Liying Jiang, Jiafeng Li, L. Zhuo, Ziqi Zhu

{"title":"Robust Vehicle Classification Based on the Combination of Deep Features and Handcrafted Features","authors":"Liying Jiang, Jiafeng Li, L. Zhuo, Ziqi Zhu","doi":"10.1109/Trustcom/BigDataSE/ICESS.2017.323","DOIUrl":"https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.323","url":null,"abstract":"Vehicle classification plays an important part in Intelligent Transport System. Recently, deep learning has showed outstanding performance in image classification. However, numerous parameters of the deep network need to be optimized which is time-consuming. PCANet is a light-weight deep learning network that is easy to train. In this paper, a new robust vehicle classification method is proposed, in which the deep features of PCANet, handcrafted features of HOG (Histogram of Oriented Gradient) and HU moments are extracted to describe the content property of vehicles. In addition, the spatial location information is introduced to HU moments to improve its distinguishing ability. The combined features are input to SVM (Support Vector Machine) to train the classification model. The vehicles are classified into six categories, i.e. large bus, car, motorcycle, minibus, truck and van. We construct a VehicleDataset including 13700 vehicle images extracted from real surveillance videos to carry out the experiments. The average classification accuracy can achieve 98.34%, which is 4.49% higher than that obtained from the conventional methods based on \"Feature + Classifier\" and is also slightly higher than that from GoogLeNet (98.26%). The proposed method doesn't need GPU and has much greater convenience than GoogLeNet. The experimental results have demonstrated that for a specific task, the combination of the deep features obtained from light-weight deep learning network and the handcrafted features can achieve comparable or even higher performance compared to the deeper neural network.","PeriodicalId":170253,"journal":{"name":"2017 IEEE Trustcom/BigDataSE/ICESS","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123326874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Mediator-Based Immediate Attribute Revocation Mechanism for CP-ABE in Multicast Group Communications 多播组通信中基于中介的CP-ABE即时属性撤销机制

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.252

Lyes Touati, Y. Challal

引用次数: 1

Improving Leakage Path Coverage in Android Apps 改进Android应用程序的泄漏路径覆盖率

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.258

G. Modi, V. Laxmi, Smita Naval, M. Gaur

{"title":"Improving Leakage Path Coverage in Android Apps","authors":"G. Modi, V. Laxmi, Smita Naval, M. Gaur","doi":"10.1109/Trustcom/BigDataSE/ICESS.2017.258","DOIUrl":"https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.258","url":null,"abstract":"With the phenomenal increase in Android apps usage and storing of personal information on mobile devices, securing this sensitive information has assumed significance. The Android application developers knowingly or unknowingly create apps that may directly or indirectly leak this information to outside world. The majority of state-of-the-art approachesdetect leaks through inter-component communication (ICC) within an app. Android allows inter-component communication (ICC) within the components of the same application or across multiple applications. ICC mechanism is used for the exchange of information among apps. Via ICC, an app or a set of apps can send the sensitive information out of the application or device.In this paper, we propose an approach for intra-app as well as inter-app data transfer analysis through intents and/or sharedpreferences that improve the coverage of leakage paths detectedas compared to existing approaches. Our proposed approach iscapable of analyzing more than two applications at a time. Wehave evaluated proposed approach on the DroidBench datasetand 116 real-time apps randomly selected and downloadedfrom Google PlayStore. We detected 1298 inter-component pathswithin an app and 215 inter-app sensitive paths. Our approachreported ~17.71% of more inter-component paths using sharedpreferences for data transfer.","PeriodicalId":170253,"journal":{"name":"2017 IEEE Trustcom/BigDataSE/ICESS","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128744528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NodeLeaper: Lower Overhead Oblivious AVL Tree NodeLeaper:低开销无关联AVL树

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.275

Yao Liu, Qingkai Zeng, Pinghai Yuan

{"title":"NodeLeaper: Lower Overhead Oblivious AVL Tree","authors":"Yao Liu, Qingkai Zeng, Pinghai Yuan","doi":"10.1109/Trustcom/BigDataSE/ICESS.2017.275","DOIUrl":"https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.275","url":null,"abstract":"Obliviousness is crypto primitives which intent to hide access pattern. Although ORAM is strongest crypto model, it incurs significant overhead. Elaine Shi et. al. propose Obliviousness Data Structrue (ODS) that makes a great theriotical improvement comparing to general ORAM algorithm, in case of the data blocks exhibit some degree of access predictability. Take AVL tree as an example, when all data blocks are organized as one AVL tree, every nodes (data blocks) contain position information points to both of its child node. As such, the client can immediately obtain the next position to be accessed instead of issuing another ORAM access to the server for a PosMap lookup. Also, the algorithm need extra client space for updating the AVL tree.In this paper, we introduce oblivious AVL tree NodeLeaper, NodeLeaper for short, which enables position information of all child nodes to share part of bits. As such one can store multiple positions for is child and grandson node positions with same block size. In this way, the search can be processed in a leap manner. As a result, NodeLeaper theriotically needs less ORAM accessand client space for node updating than ODS.","PeriodicalId":170253,"journal":{"name":"2017 IEEE Trustcom/BigDataSE/ICESS","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125894427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Privacy-Preserving Outsourcing of Large-Scale QR Factorization 大规模QR分解的高效隐私保护外包

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.331

Changqing Luo, Kaijin Zhang, Sergio Salinas, Pan Li

{"title":"Efficient Privacy-Preserving Outsourcing of Large-Scale QR Factorization","authors":"Changqing Luo, Kaijin Zhang, Sergio Salinas, Pan Li","doi":"10.1109/Trustcom/BigDataSE/ICESS.2017.331","DOIUrl":"https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.331","url":null,"abstract":"Modern organizations have collected vast amounts of data created by various systems and applications. Scientists and engineers have a strong desire to advance scientific and engineering knowledge from such massive data. QR factorization is one of the most fundamental mathematical tools for data analysis. However, conducting QR factorization of a matrix requires high computational complexity. This incurs a formidable challenge in efficiently analyzing large-scale data sets by normal users or small companies on traditional resource limited computers. To overcome this limitation, industry and academia propose to employ cloud computing that can offer abundant computing resources. This, however, raises privacy concerns because users' data may contain sensitive information that needs to be hidden for ethical, legal, or security reasons. To this end, we propose a privacy-preserving outsourcing algorithm for efficiently performing large-scale QR factorization. We implement the proposed algorithm on the Amazon Elastic Compute Cloud (EC2) platform and a laptop. The experiment results show significant time saving for the user.","PeriodicalId":170253,"journal":{"name":"2017 IEEE Trustcom/BigDataSE/ICESS","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127100314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

A Fault-based Attack on AEZ v4.2 基于故障的AEZ v4.2攻击

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.294

Hassan Qahur Al Mahri, L. Simpson, Harry Bartlett, E. Dawson, Kenneth Koon-Ho Wong

引用次数: 2

Fine-Grained Fingerprinting Threats to Software-Defined Networks 软件定义网络的细粒度指纹识别威胁

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.229

Minjian Zhang, Jianwei Hou, Ziqi Zhang, Wenchang Shi, Bo Qin, Bin Liang

{"title":"Fine-Grained Fingerprinting Threats to Software-Defined Networks","authors":"Minjian Zhang, Jianwei Hou, Ziqi Zhang, Wenchang Shi, Bo Qin, Bin Liang","doi":"10.1109/Trustcom/BigDataSE/ICESS.2017.229","DOIUrl":"https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.229","url":null,"abstract":"Thanks to its flexibility and programmable features, Software-Defined Networking (SDN) has been attracting more and more attention from the academia and the industry. Unfortunately, the fundamental characteristic of SDN that decouples control plane from data plane becomes a potential attack surface as well, which enables adversaries to fingerprint and attack the SDNs. Existing work showed the possibility of fingerprinting an SDN with time-based features. However, they are coarse grained. This paper proposes a fine-grained fingerprinting approach and reveals the much more severe threats to SDN Security. By analyzing network packets, the approach digs out match fields of SDN flow rules innovatively. Being sensitive and control-related information in SDN, the match fields of flow rules can be used to infer the type of an SDN controller and the security policy of the network. With these sensitive configuration information, adversaries can launch more targeted and destructive attacks against an SDN. We implement our approach in both simulative and physical environments. Furthermore, we conduct experiments with different kinds of SDN controllers to verify the effectiveness of our concept. Experiment results demonstrate the feasibility to obtain highly sensitive, fine-grained information in SDN, and hence reveal the high risk of information disclosure in SDN and severe threats of attacks against SDN.","PeriodicalId":170253,"journal":{"name":"2017 IEEE Trustcom/BigDataSE/ICESS","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133809492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8