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Tinman: Eliminating confidential mobile data exposure with security oriented offloading 15

Tinman: Eliminating confidential mobile data exposure with s...

引用

10th European Conference on computer systems, EuroSys 2015

作者： Xia, Yubin Liu, Yutao Tan, Cheng Ma, Mingyang Guan, Haibing Zang, Binyu Chen, Haibo Shanghai Key Laboratory of Scalable Computing and Systems China Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China Department of Computer Science Shanghai Jiao Tong University China

ISBN: (纸本)9781450332385

The wide adoption of smart devices has stimulated a fast shift of security-critical data from desktop to mobile devices. However, recurrent device theft and loss expose mobile devices to various security threats and even physical attacks. This paper presents TinMan, a system that protects confidential data such as web site password and credit card number (we use the term cor to represent these data, which is short for Confidential Record) from being leaked or abused even under device theft. TinMan separates accesses of cor from the rest of the functionalities of an app, by introducing a trusted node to store cor and offloading any code from a mobile device to the trusted node to access cor. This completely eliminates the exposure of cor on the mobile devices. The key challenges to TinMan include deciding when and how to efficiently and transparently offload execution;Tin-Man addresses these challenges with security-oriented offloading with a low-overhead tainting scheme called asymmetric tainting to track accesses to cor to trigger offloading, as well as transparent SSL session injection and TCP payload replacement to offload accesses to cor. We have implemented a prototype of TinMan based on Android and demonstrated how TinMan protects the information of user's bank account and credit card number without modifying the apps. Evaluation results also show that TinMan incurs only a small amount of performance and power overhead. Copyright © 2015 ACM.

关键词： Crime

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Detecting process transitions from wearable sensors: An unsupervised labeling approach 17

Detecting process transitions from wearable sensors: An unsu...

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4th international Workshop on Sensor-Based Activity Recognition and Interaction, iWOAR 2017

作者： Böttcher, Sebastian Scholl, Philipp M. Van Laerhoven, Kristof Medical Center University of Freiburg Department of Neurosurgery Freiburg Germany Embedded Systems University of Freiburg Computer Science Institute Freiburg Germany Ubiquitous Computing University of Siegen Electrical Engineering and Computer Science Siegen Germany

ISBN: (纸本)9781450352239

Authoring protocols for manual tasks such as following recipes, manufacturing processes, or laboratory experiments requires a significant effort. This paper presents a system that estimates individual procedure transitions from the user's physical movement and gestures recorded with inertial motion sensors. Combined with egocentric or external video recordings this facilitates efficient review and annotation of video databases. We investigate different clustering algorithms on wearable inertial sensor data recorded on par with video data, to automatically create transition marks between task steps. The goal is to match these marks to the transitions given in a description of the workflow, thus creating navigation cues to browse video repositories of manual work. To evaluate the performance of unsupervised clustering algorithms, the automatically generated marks are compared to human-expert created labels on publicly available datasets. Additionally, we tested the approach on a novel data set in a manufacturing lab environment, describing an existing sequential manufacturing process. © 2017 Association for Computing Machinery.

关键词： Video recording

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Automatic loop transformation selection with the Aid of kohonen's self-organizing maps for parallelizing compilers

Automatic loop transformation selection with the Aid of koho...

引用

2008 International Conference on Parallel and distributed Processing Techniques and Applications, PDPTA 2008

作者： Mead, Mohamed Ahamed Eldeeb, Hesham Nassar, Salwa Computer Science Mathematics Department Faculty of Science Cairo University Cairo Egypt Computers and Systems Department Electronics Research Institute Dokki Giza Egypt Head of Parallel and Distributed Systems Team Computers and Systems Department Electronics Research Institute Dokki Giza Egypt

ISBN: (纸本)1601320841

Determine the appropriate loop transformations is an essential process in the automatic parallelization field. The sequence of loop transformation to be applied also must be considered. Selection of loop transformations faces many challenges, it is needed an experienced. In this paper, An Intelligent Loop Transformation Selector (ILTS);as a part of parallelizing tool project, was developed to overcome on these challenges and imitate an experienced. A Kohonen's Self-Organizing Map (SOM) neural network is used to select the appropriate loop transformation or sequence of them. Neural Networks offer intelligent transformations selection to reduce or eliminate the dependencies and maximize the parallelization in the sequential code. The experimental results show that ILTS chooses loop transformations successfully in most cases. This tool can be integrated with any parallelizing compiler to enhance loop transformation selection process.

关键词： Conformal mapping

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Application of computational intelligence techniques in active networks 01

Application of computational intelligence techniques in acti...

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2001 ACM Symposium on Applied Computing, SAC 2001

作者： Vasilakos, Athanasios V. Anagnostakis, Kostas G. Pedrycz, Witold Institute of Computer Science Foundation of Research and Technology Hellas Greece Distributed Systems Lab CIS Department Universityof Pennsylvania United States Department of Electrical and Computer Engineering University of Alberta Canada

ISBN: (纸本)1581132875

Computational intelligence techniques have been successfully used for solving control problems in p ache t-switching network architectures. The introduction of active networking adds a high degree of flexibility in customizing the network infrastructure and introducing new functionality. Therefore, there is a clear need for investigating both the applicability of computational intelligence techniques in this new networking environment, as well as the provisions of active networking technology that computational intelligence techniques can exploit for improved operation. We report on the characteristics of these technologies, their synergy and on outline recent efforts in the design of a computational intelligence toolkit and its application to routing on a novel active networking environment. © 2001 ACM.

关键词： Artificial intelligence

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Static analysis of worst-case stack cache behavior 13

Static analysis of worst-case stack cache behavior

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21st International Conference on Real-Time Networks and systems, RTNS 2013

作者： Jordan, Alexander Brandner, Florian Schoeberl, Martin Compilers and Languages Group Institute of Computer Languages Vienna University of Technology Austria Embedded Systems Engineering Section Dep. of Applied Mathematics and Computer Science Technical University of Denmark Denmark

ISBN: (纸本)9781450320580

Utilizing a stack cache in a real-time system can aid predictability by avoiding interference that heap memory traffic causes on the data cache. While loads and stores are guaranteed cache hits, explicit operations are responsible for managing the stack cache. The behavior of these operations can be analyzed statically. We present algorithms that derive worst-case bounds on the latency-inducing operations of the stack cache. Their results can be used by a static WCET tool. By breaking the analysis down into subproblems that solve intra-procedural data-flow analysis and path searches on the call-graph, the worst-case bounds can be efficiently yet precisely determined. Our evaluation using the MiBench benchmark suite shows that only 37% and 21% of potential stack cache operations actually store to and load from memory, respectively. Analysis times are modest, on average running between 0.46s and 1.30s per benchmark, depending on the size of the stack cache. © 2013 ACM.

关键词： Real time systems

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Organic and Pervasive Computing – ARCS 2004 1

引用

丛书名： Lecture Notes in computer science

1000年

作者： Christian Müller-Schloer Theo Ungerer Bernhard Bauer

ISBN: (数字)9783540247142

ISBN: (纸本)9783540212386

Where is system architecture heading? The special interest group on computer and systems Architecture (Fachausschuss Rechner- und Systemarchitektur) of the German computer and information technology associations GI and ITG a- ed this question and discussed it during two Future Workshops in 2002. The result in a nutshell: Everything will change but everything else will remain. Future systems technologies will build on a mature basis of silicon and IC technology,onwell-understoodprogramminglanguagesandsoftwareengineering techniques, and on well-established operating systems and middleware concepts. Newer and still exotic but exciting technologies like quantum computing and DNA processing are to be watched closely but they will not be mainstream in the next decade. Although there will be considerable progress in these basic technologies, is there any major trend which uni?es these diverse developments? There is a common denominator – according to the result of the two - ture Workshops – which marks a new quality. The challenge for future systems technologies lies in the mastering of complexity. Rigid and in?exible systems, built under a strict top-down regime, have reached the limits of manageable complexity, as has become obvious by the recent failure of several large-scale projects. Nature is the most complex system we know, and she has solved the problem somehow. We just haven’t understood exactly how nature does it. But it is clear that systems designed by nature, like an anthill or a beehive or a swarm of birds or a city, are di?erent from today’s technical systems that have beendesignedbyengineersandcomputerscientists.

关键词： computer System Implementation computer Communication Networks Software Engineering Operating systems Information Storage and Retrieval Information systems Applications (incl. Internet)

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Training Adversarially Robust Sparse Networks via Bayesian Connectivity Sampling 38

Training Adversarially Robust Sparse Networks via Bayesian C...

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38th International Conference on Machine Learning, ICML 2021

作者： Özdenizci, Ozan Legenstein, Robert Graz University of Technology Institute of Theoretical Computer Science Graz Austria Silicon Austria Labs TU Graz - SAL Dependable Embedded Systems Lab Graz Austria

ISBN: (纸本)9781713845065

Deep neural networks have been shown to be susceptible to adversarial attacks. This lack of adversarial robustness is even more pronounced when models are compressed in order to meet hardware limitations. Hence, if adversarial robustness is an issue, training of sparsely connected networks necessitates considering adversarially robust sparse learning. Motivated by the efficient and stable computational function of the brain in the presence of a highly dynamic synaptic connectivity structure, we propose an intrinsically sparse rewiring approach to train neural networks with state-of-the-art robust learning objectives under high sparsity. Importantly, in contrast to previously proposed pruning techniques, our approach satisfies global connectivity constraints throughout robust optimization, i.e., it does not require dense pre-training followed by pruning. Based on a Bayesian posterior sampling principle, a network rewiring process simultaneously learns the sparse connectivity structure and the robustness-accuracy trade-off based on the adversarial learning objective. Although our networks are sparsely connected throughout the whole training process, our experimental benchmark evaluations show that their performance is superior to recently proposed robustness-aware network pruning methods which start from densely connected networks. Copyright © 2021 by the author(s)

关键词： Deep neural networks

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New speed records for Salsa20 stream cipher using an autotuning framework on GPUs

New speed records for Salsa20 stream cipher using an autotun...

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6th International Conference on the Theory and Application of Cryptographic Techniques in Africa, AFRICACRYPT 2013

作者： Khalid, Ayesha Paul, Goutam Chattopadhyay, Anupam Institute for Communication Technologies and Embedded Systems RWTH Aachen University Aachen 52074 Germany Department of Computer Science and Engineering Jadavpur University Kolkata 700 032 India

ISBN: (纸本)9783642385520

Since the introduction of the CUDA programming model, GPUs are considered a viable platform for accelerating non-graphical applications. Many cryptographic algorithms have been reported to achieve remarkable performance speedups, especially block ciphers. For stream ciphers, however, the lack of reported GPU acceleration endeavors is due to their inherent iterative structures that prohibit parallelization. In this paper, we propose an efficient implementation methodology for data-parallel cryptographic functions in a batch processing fashion on modern GPUs in general and optimizations for Salsa20 in particular. We present an autotuning framework to reach the most optimized set of device and application parameters for Salsa20 kernel variants with throughput maximization as a figure of merit. The peak performance achieved by our implementation for Salsa20/12 is 2.7 GBps and 43.44 GBps with and without memory transfers respectively on NVIDIA GeForce GTX 590. These figures beat the fastest reported GPU implementation of any stream cipher in the eSTREAM portfolio including Salsa20/12, as well as the block cipher AES optimized by hand-tuning, and thus, to the best of our knowledge set a new speed record. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Graphics processing unit

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Optimized GPU implementation and performance analysis of HC series of stream ciphers

Optimized GPU implementation and performance analysis of HC ...

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15th International Conference on Information Security and Cryptology, ICISC 2012

作者： Khalid, Ayesha Bagchi, Deblin Paul, Goutam Chattopadhyay, Anupam Institute for Communication Technologies and Embedded Systems RWTH Aachen University Aachen 52074 Germany Department of Computer Science and Engineering Jadavpur University Kolkata 700 032 India

ISBN: (纸本)9783642376818

The ease of programming offered by the CUDA programming model attracted a lot of programmers to try the platform for acceleration of many non-graphics applications. Cryptography, being no exception, also found its share of exploration efforts, especially block ciphers. In this contribution we present a detailed walk-through of effective mapping of HC-128 and HC-256 stream ciphers on GPUs. Due to inherent inter-S-Box dependencies, intra-S-Box dependencies and a high number of memory accesses per keystream word generation, parallelization of HC series of stream ciphers remains challenging. For the first time, we present various optimization strategies for HC-128 and HC-256 speedup in tune with CUDA device architecture. The peak performance achieved with a single data-stream for HC-128 and HC-256 is 0.95 Gbps and 0.41 Gbps respectively. Although these throughput figures do not beat the CPU performance (10.9 Gbps for HC-128 and 7.5 Gbps for HC-256), our multiple parallel data-stream implementation is benchmarked to reach approximately 31 Gbps for HC-128 and 14 Gbps for HC-256 (with 32768 parallel data-streams). To the best of our knowledge, this is the first reported effort of mapping HC-Series of stream ciphers on GPUs. © 2013 Springer-Verlag.

关键词： Graphics processing unit

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Distilled Neural Network for Clothes Type Identification 20

Distilled Neural Network for Clothes Type Identification

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20th International Conference on Electrical Engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Chongpiphattanasiri, Teerapon Wickramatilake, Pramod Moonrinta, Jednipat Ekpanyapong, Mongkol Dailey, Matthew N. Asian Institute of Technology Microelectronics and Embedded Systems Pathumthani Thailand Asian Institute of Technology Artificial Intelligence Center Pathumthani Thailand Asian Institute of Technology Computer Science and Information Management Pathumthani Thailand

ISBN: (纸本)9798350310467

Diverse applications such as criminal forensics and retail customer market segmentation would benefit from the ability to track individuals across multiple cameras. Clothing information is one of the keys to this capability when other identifying information such as the face cannot be reliably extracted. To quickly search for a criminal suspect or re-identify individuals across time and space, filtering by clothing over a given set of cameras for a given time range can dramatically narrow the search. To achieve this capability, we propose a training pipeline that adapts a knowledge distillation neural network to develop a lightweight but effective deep learning model for classifying clothing. The proposed training pipeline produces a small model that achieves 78.70% clothes type classification accuracy on the DeepFashion2 benchmark dataset. An experimental inference time of 0.019 per image on a Nvidia RTX 2070 Super GPU demonstrates that the system has potential for deployment over large-scale camera sensor networks. We expect the new approach to accelerate criminal investigations and enable other analytics requiring reidentification of individuals across multiple cameras at scale. © 2023 IEEE.

关键词： Pipelines

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