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检索条件"任意字段=Proceedings of the Fifteenth ACM Conference on Data and Application Security and Privacy"

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9th International Workshop on Artificial Intelligence and security: AISec 2016 16

9th International Workshop on Artificial Intelligence and Se...

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proceedings of the 2016 acm SIGSAC conference on Computer and Communications security

作者： David Mandell Freeman Katerina Mitrokotsa Arunesh Sinha LinkedIn Corporation Mountain View CA USA Chalmers University of Technology Gothenburg Sweden University of Michigan Ann Arbor MI USA

ISBN: (纸本)9781450341394

Artificial Intelligence (AI) and Machine Learning (ML) provide a set of useful analytic and decision-making techniques that are being leveraged by an ever-growing community of practitioners, including many whose applications have security-sensitive elements. However, while security researchers often utilize such techniques to address problems and AI/ML researchers develop techniques for Big data analytics applications, neither community devotes much attention to the other. Within security research, AI/ML components are usually regarded as black-box solvers. Conversely, the learning community seldom considers the security/privacy implications entailed in the application of their algorithms when they are designing them. While these two communities generally focus on different directions, where these two fields do meet, interesting problems appear. Researchers working in this intersection have raised many novel questions for both communities and created a new branch of research known as secure learning. The AISec workshop has become the primary venue for this unique fusion of research. In recent years, there has been an increase of activity within the AISec/secure learning community. There are several reasons for this surge. Firstly, machine learning, data mining, and other artificial intelligence technologies play a key role in extracting knowledge, situational awareness, and security intelligence from Big data. Secondly, companies like Google, Facebook, Amazon, and Splunk are increasingly exploring and deploying learning technologies to address Big data problems for their customers.

关键词： privacy artificial intelligence security machine learning

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A network security game model 15

A network security game model

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5th acm conference on data and application security and privacy, CODASPY 2015

作者： Shandilya, Vivek Shiva, Sajjan Dept of Computer Science University of Memphis MemphisTN38152 United States

ISBN: (纸本)9781450331913

There have been attempts to model the interaction between users, both malicious and benign, and network administrators as games. Building on such works, we here present a game model which is generic enough to capture various modes of such interactions. The model facilitates stochastic games with imperfect information. The imperfect information is due to erroneous sensors leading to incorrect perception of the current state by the players. To model this error in perception distributed over other multiple states, we use Euclidean distances between inputs from the sensors. Copyright © 2015 acm.

关键词： Network security

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AppSec: A Safe Execution Environment for security Sensitive applicationst 15

AppSec: A Safe Execution Environment for Security Sensitive ...

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11th acm SIGPLAN/SIGOPS International conference on Virtual Execution Environments

作者： Ren, Jianbao Qi, Yong Dai, Yuehua Wang, Xiaoguang Shi, Yi Xi An Jiao Tong Univ Xian Peoples R China

ISBN: (纸本)9781450334501

Malicious OS kernel can easily access user's private data in main memory and pries human-machine interaction data, even one that employs privacy enforcement based on application level or OS level. This paper introduces AppSec, a hypervisor-based safe execution environment, to protect both the memory data and human-machine interaction data of security sensitive applications from the untrusted OS transparently. AppSec provides several security mechanisms on an untrusted OS. AppSec introduces a safe loader to check the code integrity of application and dynamic shared objects. During runtime, AppSec protects application and dynamic shared objects from being modified and verifies kernel memory accesses according to application's intention. AppSec provides a devices isolation mechanism to prevent the human-machine interaction devices being accessed by compromised kernel. On top of that, AppSec further provides a privileged-based window system to protect application's X resources. The major advantages of AppSec are threefold. First, AppSec verifies and protects all dynamic shared objects during runtime. Second, AppSec mediates kernel memory access according to application's intention but does not encrypts all application's data roughly. Third, AppSec provides a trusted I/O path from end-user to application. A prototype of AppSec is implemented and shows that AppSec is efficient and practical.

关键词： Design security Performance privacy VMM Kernel Human-machine interaction

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Information sharing and user privacy in the third-party identity management landscape 15

Information sharing and user privacy in the third-party iden...

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5th acm conference on data and application security and privacy, CODASPY 2015

作者： Vapen, Anna Carlsson, Niklas Mahanti, Anirban Shahmehri, Nahid Linköping University Sweden NICTA Australia

ISBN: (纸本)9781450331913

Third-party identity management services enable cross-site information sharing, making Web access seamless but also raise significant privacy implications for the users. Using a combination of manual analysis of identified third-party identity management relationships and targeted case stud- ies we capture how the protocol usage and third-party se- lection is changing, profile what information is requested to be shared (and actions to be performed) between web- sites, and identify privacy issues and practical problems that occur when using multiple accounts (associated with these services). The study highlights differences in the privacy leakage risks associated with different classes of websites, and shows that the use of multiple third-party websites, in many cases, can cause the user to lose (at least) partial con- trol over which information is shared/posted on their behalf. Copyright © 2015 acm.

关键词： Websites

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privacy-preserving scanning of big content for sensitive data exposure with mapreduce 15

Privacy-preserving scanning of big content for sensitive dat...

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5th acm conference on data and application security and privacy, CODASPY 2015

作者： Liu, Fang Shu, Xiaokui Yao, Danfeng Daphne Butt, Ali R. Department Computer Science Virginia Tech BlacksburgVA United States

ISBN: (纸本)9781450331913

The exposure of sensitive data in storage and transmission poses a serious threat to organizational and personal secu- rity. data leak detection aims at scanning content (in stor- age or transmission) for exposed sensitive data. Because of the large content and data volume, such a screening algo- rithm needs to be scalable for a timely detection. Our solu- tion uses the MapReduce framework for detecting exposed sensitive content, because it has the ability to arbitrarily scale and utilize public resources for the task, such as Ama- zon EC2. We design new MapReduce algorithms for com- puting collection intersection for data leak detection. Our prototype implemented with the Hadoop system achieves 225 Mbps analysis throughput with 24 nodes. Our algo- rithms support a useful privacy-preserving data transfor- mation. This transformation enables the privacy-preserving technique to minimize the exposure of sensitive data during the detection. This transformation supports the secure out- sourcing of the data leak detection to untrusted MapReduce and cloud providers. Copyright © 2015 acm.

关键词： MapReduce

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Secure information and resource sharing in cloud 15

Secure information and resource sharing in cloud

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5th acm conference on data and application security and privacy, CODASPY 2015

作者： Zhang, Yun Krishnan, Ram Sandhu, Ravi Institute for Cyber Security Univ of Texas at San Antonio San AntonioTX78249 United States Dept. of Electrical and Computer Engineering Univ of Texas at San Antonio San AntonioTX78249 United States

ISBN: (纸本)9781450331913

The significant threats from information security breaches in cyber world is one of the most serious security problems. Organizations are facing growing number of sophisticated cyber-attacks every year. Efficient and secure sharing of at- tack and security information during cyber incident response plays increasingly significant role in fixing the problems as well as helping organizations recover fast. While traditional systems are slow and inefficient in sharing information and resources securely, cloud platform provides us a consider- able convenience to facilitate the sharing. In this paper, we propose access control models for secure information and re- source sharing (IARS) in cloud Infrastructure as a Service (IaaS). Copyright © 2015 acm.

关键词： Infrastructure as a service (IaaS)

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Combining ORAM with PIR to minimize bandwidth costs 15

Combining ORAM with PIR to minimize bandwidth costs

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5th acm conference on data and application security and privacy, CODASPY 2015

作者： Dautrich, Jonathan Ravishankar, Chinya Google Inc. IrvineCA United States Computer Science and Engineering University of California Riverside United States

ISBN: (纸本)9781450331913

Cloud computing allows customers to outsource the burden of data management and benefit from economy of scale, but privacy concerns limit its reach. Even if the stored data are encrypted, access patterns may leak valuable information. Oblivious RAM (ORAM) protocols guarantee full access pattern privacy, but even the most efficient ORAMs proposed to date incur large bandwidth costs. We combine Private Information Retrieval (PIR) techniques with the most bandwidth-efficient existing ORAM scheme known to date (ObliviStore), to create OS+PIR, a new ORAM with bandwidth costs only half those of ObliviStore. For data block counts ranging from 220 to 230, OS+PIR achieves a total bandwidth cost of only 11X-13X blocks transferred per client block read+write, down from Oblivi- Store's 18X-26X. OS+PIR introduces several enhancements in addition to PIR in order to achieve its lower costs, including mechanisms for eliminating unused dummy blocks. Copyright © 2015 acm.

关键词： data privacy

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An Approach to Designing a Network security-based application for Communications Safety

An Approach to Designing a Network Security-based Applicatio...

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IEEE/acm International conference on Advances in Social Networks Analysis and Mining (ASONAM)

作者： Bruce, Ndibanje Kang, Young Jin Sain, Mangal Lee, Hoon Jae Dongseo Univ Grad Sch Dept Ubiquitous IT Busan 617716 South Korea Dongseo Univ Div Comp & Engn Busan 617716 South Korea

ISBN: (纸本)9781450338547

The evolution of digital communication includes both applications and devices running them. In this context, specific applications are needed to enhance a safeguard communication ensuring protection and trust services to the users. The growing need to address privacy concerns when social network data is released for mining purposes has recently led to considerable interest in various network security-based applications. In this paper, we develop an efficiency and adaptive network security-based application to ensure the privacy and data integrity across channel communications. This approach is designed on a model of clustering configuration of the involved members. The cluster members interact with the cluster leader for data exchange and sends to the base station. This scheme provides a strong mutual authentication framework that suits for real heterogeneous wireless applications. In addition, we contribute with a mathematical analysis to the delay and optimization analysis in a clustering topology node-based, also we compared the scheme with existing ones using security standards services. Finally, performance of this scheme is evaluated in term of computation and communication overhead;results show the present framework is efficiency and can be safeguard for Network security-based applications.

关键词： network security applications algorithms protocols mutual authentication

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SemaDroid: A privacy-aware sensor management framework for smartphones 15

SemaDroid: A privacy-aware sensor management framework for s...

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5th acm conference on data and application security and privacy, CODASPY 2015

作者： Xu, Zhi Zhu, Sencun Pennsylvania State University University ParkPA United States

ISBN: (纸本)9781450331913

While mobile sensing applications are booming, the sensor man- agement mechanisms in current smartphone operating systems are left behind - they are incomprehensive and coarse-grained, expos- ing a huge attack surface for malicious or aggressive third party apps to steal user's private information through mobile sensors. In this paper, we propose a privacy-aware sensor management framework, called SemaDroid, which extends the existing sensor management framework on Android to provide comprehensive and fine-grained access control over onboard sensors. SemaDroid al- lows the user to monitor the sensor usage of installed apps, and to control the disclosure of sensing information while not affect- ing the app's usability. Furthermore, SemaDroid supports context- aware and quality-of-sensing based access control policies. The en- forcement and update of the policies are in real-time. Detailed de- sign and implementation of SemaDroid on Android are presented to show that SemaDroid works compatible with the existing An- droid security framework. Demonstrations are also given to show the capability of SemaDroid on sensor management and on defeat- ing emerging sensor-based attacks. Finally, we show the high effi- ciency and security of SemaDroid. Copyright © 2015 acm.

关键词： Smartphones

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Rapid screening of transformed data leaks with efficient algorithms and parallel computing 15

Rapid screening of transformed data leaks with efficient alg...

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5th acm conference on data and application security and privacy, CODASPY 2015

作者： Shu, Xiaokui Zhang, Jing Yao, Danfeng Daphne Feng, Wu-Chun Department of Computer Science Virginia Tech BlacksburgVA United States

ISBN: (纸本)9781450331913

The leak of sensitive data on computer systems poses a seri- ous threat to organizational security. Organizations need to identify the exposure of sensitive data by screening the con- tent in storage and transmission, i.e., to detect sensitive in- formation being stored or transmitted in the clear. However, detecting the exposure of sensitive information is challenging due to data transformation in the content. Transformations (such as insertion, deletion) result in highly unpredictable leak patterns. Existing automata-based string matching algorithms are impractical for detecting transformed data leaks, because of its formidable complexity when model- ing the required regular expressions. We design two new algorithms for detecting long and transformed data leaks. Our system achieves high detection accuracy in recogniz- ing transformed leaks compared to the state-of-the-art in- spection methods. We parallelize our prototype on graphics processing unit and demonstrate the strong scalability of our detection solution required by a sizable organization. Copyright © 2015 acm.

关键词： Sensitive data

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