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

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Practical UC-Secure Delegatable Credentials with Attributes and Their application to Blockchain 17

Practical UC-Secure Delegatable Credentials with Attributes ...

引用

24th acm-SIGSAC conference on Computer and Communications security (acm CCS)

作者： Camenisch, Jan Drijvers, Manu Dubovitskaya, Maria IBM Res Zurich Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781450349468

Certification of keys and attributes is in practice typically realized by a hierarchy of issuers. Revealing the full chain of issuers for certificate verification, however, can be a privacy issue since it can leak sensitive information about the issuer's organizational structure or about the certificate owner. Delegatable anonymous credentials solve this problem and allow one to hide the full delegation (issuance) chain, providing privacy during both delegation and presentation of certificates. However, the existing delegatable credentials schemes are not efficient enough for practical use. In this paper, we present the first hierarchical (or delegatable) anonymous credential system that is practical. To this end, we provide a surprisingly simple ideal functionality for delegatable credentials and present a generic construction that we prove secure in the UC model. We then give a concrete instantiation using a recent pairing-based signature scheme by Groth and describe a number of optimizations and efficiency improvements that can be made when implementing our concrete scheme. The latter might be of independent interest for other pairing-based schemes as well. Finally, we report on an implementation of our scheme in the context of transaction authentication for blockchain, and provide concrete performance figures.

关键词： Credentials Delegation Hierarchical issuance privacy-preserving authentication Composable security Zero-knowledge Blockchain

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Research Issues and Approaches for Connected and Automated Vehicles 17

Research Issues and Approaches for Connected and Automated V...

引用

7th acm conference on data and application security and privacy (CODASPY)

作者： Shin, Kang G. Univ Michigan Ann Arbor Ann Arbor MI 48109 USA

ISBN: (纸本)9781450345231

Driverless and/or environment-friendly cars have recently received a great deal of attention from media and almost all industry and government sectors due mainly to their great potential impacts on safety, economy, and environments. In particular, enabling vehicles to communicate with one another via wireless devices holds the potential to automate vehicles while dramatically improving safety, reducing congestion, and conserving energy. To move toward realization of this potential, we have been conducting research into various issues, including: Automation of vehicle sensing and control;Vehicle safety and passenger comfort;Securing information communication and computation;Developing environment-friendly solutions. In this talk, I will discuss various issues and approaches related to security of connected and automated vehicles.

关键词： Connected and/or automated cars security of in-vehicle networks intrusion detection

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POSTER: TTPP Method for Validation of k-anonymity on Wi-Fi Association Log data 10

POSTER: TTPP Method for Validation of k-anonymity on Wi-Fi A...

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10th acm conference on security and privacy in Wireless and Mobile Networks (WiSec)

作者： Fukuzaki, Akinobu Koide, Noriaki Sonehara, Noboru Natl Inst Informat Chiyoda Ku 2-1-2-2008 Hitotsubashi Tokyo Japan Tsuda Univ Shibuya Ku 1-18-24 Sendagaya Tokyo Japan

ISBN: (纸本)9781450350846

Using data about individuals without revealing sensitive information about them is important. In recently years, a new privacy protection concept is called k-anonymity has been introduced. On the other hand, application of person trip data analysis is demanded for public policy making such as tourism and security. In this research, TTPP and Kn-Query method is introduced to solved a conflict between privacy protection and utilization of person trip data. TTPP method is proposed as a data structure which describes person trip using the paired entries of fixed point observed personal location with track ID, time window and place. Kn-Query is a query summarizing the number of samples under given conditions satisfying k-anonymity. In an ordinal method, validation of k-anonymity and person trip analysis have been considered separately. The proposed method solved a conflict between privacy and utilization of personal data.

关键词： Wi-Fi sensing privacy Social data

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A Secure, privacy-Preserving IoT Middleware Using Intel SGX 17

A Secure, Privacy-Preserving IoT Middleware Using Intel SGX

引用

7th International conference on the Internet of Things

作者： Gremaud, Pascal Durand, Arnaud Pasquier, Jacques Univ Fribourg Fribourg Switzerland

ISBN: (纸本)9781450353182

With Internet of Things (IoT) middleware solutions moving towards cloud computing, the problems of trust in cloud platforms and data privacy need to be solved. The emergence of Trusted Execution Environments (TEEs) opens new perspectives to increase security in cloud applications. We propose a privacy-preserving IoT middleware, using Intel Software Guard Extensions (SGX) to create a secure system on untrusted platforms. An encrypted index is used as a database and communication with the application is protected using asymmetric encryption. This set of measures allows our system to process events in an orchestration engine without revealing data to the hosting cloud platform.

关键词： IoT Middleware privacy-Aware Computing security Trusted Execution Environments Intel SGX

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Statistical security Incident Forensics against data Falsification in Smart Grid Advanced Metering Infrastructure 17

Statistical Security Incident Forensics against Data Falsifi...

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7th acm conference on data and application security and privacy (CODASPY)

作者： Bhattacharjee, Shameek Thakur, Aditya Silvestri, Simone Das, Sajal K. Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA Missouri Univ Sci & Technol Dept Elect Engn Rolla MO 65409 USA

ISBN: (纸本)9781450345231

Compromised smart meters reporting false power consumption data in Advanced Metering Infrastructure (AMI) may have drastic consequences on a smart grid's operations. Most existing works only deal with electricity theft from customers. However, several other types of data falsification attacks are possible, when meters are compromised by organized rivals. In this paper, we first propose a taxonomy of possible data falsification strategies such as additive, deductive, camouflage and conflict, in AMI micro-grids. Then, we devise a statistical anomaly detection technique to identify the incidence of proposed attack types, by studying their impact on the observed data. Subsequently, a trust model based on Kullback-Leibler divergence is proposed to identify compromised smart meters for additive and deductive attacks. The resultant detection rates and false alarms are minimized through a robust aggregate measure that is calculated based on the detected attack type and successfully discriminating legitimate changes from malicious ones. For conflict and camouflage attacks, a generalized linear model and Weibull function based kernel trick is used over the trust score to facilitate more accurate classification. Using real data sets collected from AMI, we investigate several trade-offs that occur between attacker's revenue and costs, as well as the margin of false data and fraction of compromised nodes. Experimental results show that our model has a high true positive detection rate, while the average false alarm rate is just 8%, for most practical attack strategies, without depending on the expensive hardware based monitoring.

关键词： Statistical Anomaly Detection security Incident Forensics Trust Models data Falsification Information Theory Supervised Learning Smart Grid Advanced Metering Infrastructure Relative Entropy

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Synthesis of Probabilistic privacy Enforcement 17

Synthesis of Probabilistic Privacy Enforcement

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24th acm-SIGSAC conference on Computer and Communications security (acm CCS)

作者： Kucera, Martin Tsankov, Petar Gehr, Timon Guarnieri, Marco Vechev, Martin Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781450349468

Existing probabilistic privacy enforcement approaches permit the execution of a program that processes sensitive data only if the information it leaks is within the bounds specified by a given policy. Thus, to extract any information, users must manually design a program that satisfies the policy. In this work, we present a novel synthesis approach that automatically transforms a program into one that complies with a given policy. Our approach consists of two ingredients. First, we phrase the problem of determining the amount of leaked information as Bayesian inference, which enables us to leverage existing probabilistic programming engines. Second, we present two synthesis procedures that add uncertainty to the program's outputs as a way of reducing the amount of leaked information: an optimal one based on SMT solving and a greedy one with quadratic running time. We implemented and evaluated our approach on 10 representative programs from multiple application domains. We show that our system can successfully synthesize a permissive enforcement mechanism for all examples.

关键词： privacy enforcement bayesian inference program synthesis

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Functional Encryption with Oblivious Helper 17

Functional Encryption with Oblivious Helper

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acm Asia conference on Computer and Communications security (ASIA CCS)

作者： Dupont, Pierre-Alain Pointcheval, David PSL Res Univ ENS CNRS DGAINRIA Paris France PSL Res Univ ENS CNRS INRIA Paris France

ISBN: (纸本)9781450349444

Functional encryption is a nice tool that bridges the gap between usability and privacy when providing access to huge databases: while being encrypted, aggregated information is available with a fine-tuned control by the owner of the database who can specify the functions he allows users to compute on the data. Unfortunately, giving access to several functions might leak too much information on the database, since once the decryption capability is given for a specific function, this is for an unlimited number of ciphertexts. In the particular case of the inner-product, if rows or records of the database contain l fields on which one got l independent inner-product capabilities, one can extract all the individual fields. On the other hand, the major applications that make use of inner-products, such as machine-learning, need to compute many of them. This paper deals with a practical trade-off in order to allow the computation of various inner-products, while still protecting the confidentiality of the data. To this aim, we introduce an oblivious helper, that will be required for any decryption-query, in order to control the leakage of information on the database. It should indeed learn just enough information to guarantee the confidentiality of the database, but without endangering the privacy of the queries.

关键词： Functional encryption oblivious decryption confidentiality privacy

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Model-Driven Software Engineering in Practice: privacy-Enhanced Filtering of Network Traffic 2017

Model-Driven Software Engineering in Practice: Privacy-Enhan...

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11th Joint Meeting of European Software Engineering conference (ESEC) / acm SIGSOFT Symposium on the Foundations of Software Engineering (FSE)

作者： van Dijk, Roel Creeten, Christophe van der Ham, Jeroen van den Bos, Jeroen Netherlands Forens Inst The Hague Netherlands Delft Univ Technol Delft Netherlands Natl Cyber Secur Ctr The Hague Netherlands Zuyd Univ Appl Sci Heerlen Netherlands

ISBN: (纸本)9781450351058

Network traffic data contains a wealth of information for use in security analysis and application development. Unfortunately, it also usually contains confidential or otherwise sensitive information, prohibiting sharing and analysis. Existing automated anonymization solutions are hard to maintain and tend to be outdated. We present privacy-Enhanced Filtering (PEF), a model-driven prototype framework that relies on declarative descriptions of protocols and a set of filter rules, which are used to automatically transform network traffic data to remove sensitive information. This paper discusses the design, implementation and application of PEF, which is available as open-source software and configured for use in a typical malware detection scenario.

关键词： model-driven engineering domain-specific languages open-source prototype privacy-enhancing technology

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10th International Workshop on Artificial Intelligence and security (AISec 2017) 17

10th International Workshop on Artificial Intelligence and S...

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24th acm-SIGSAC conference on Computer and Communications security (acm CCS)

作者： Biggio, Battista Freeman, David Miller, Brad Sinha, Arunesh Univ Cagliari Cagliari Italy Facebook Inc Menlo Pk CA USA Google Inc Mountain View CA USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781450349468

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.

关键词： Adversarial Learning Secure Learning Malware Detection

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A privacy assessment of social media aggregators 9

A privacy assessment of social media aggregators

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9th IEEE/acm International conference on Advances in Social Networks Analysis and Mining, ASONAM 2017

作者： Misra, Gaurav Such, Jose M. Gill, Lauren Security Lancaster Research Center Lancaster University Lancaster United Kingdom Department of Informatics King’s College London London United Kingdom School of Computing and Communications Lancaster University Lancaster United Kingdom

ISBN: (纸本)9781450349932

Social Media Aggregator (SMA) applications present a platform enabling users to manage multiple Social Networking Sites (SNS) in one convenient application, which results in a unique concentration of data from several SNS accounts in addition to the user’s mobile phone data available to them. In this paper, we provide a detailed privacy assessment of 13 popular SMAs from 3 app stores by using a three-step methodology by inspecting the mobile data and social media data accessed by these applications, checking for privacy policies and their compliance with distributors’ vetting policies and performing a qualitative assessment of traceability between privacy policies and the actual transparency and control mechanisms offered to users by the apps’ interfaces. Our results demonstrate a variation in data accessed by the individual applications, an absence of privacy policies for 5 of the SMAs evaluated, and a lack of traceability between privacy policies and transparency and control of interface operations. © 2017 Association for Computing Machinery.

关键词： Transparency

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