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检索条件"任意字段=Proceedings of the 5th ACM Conference on Data and Application Security and Privacy"

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Secure dot product of outsourced encrypted vectors and its application to SVM 17

Secure dot product of outsourced encrypted vectors and its a...

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5th acm International Workshop on security in Cloud Computing, SCC 2017

作者： Zhang, Jun Wang, Xin Yiu, S.M. Jiang, Zoe L. Li, Jin Department of Computer Science University of Hong Kong Pokfulam Road Hong Kong Harbin Institute of Technology Shenzhen Graduate School Shenzhen China School of Computer Science Guangzhou University Guangzhou China

ISBN: (纸本)9781450349703

It is getting popular for users to outsource their data to a cloud system as well as leverage the high-speed computing power of this third-party platform to process the data. For the sake of data privacy, outsourced data from different users is usually encrypted under different keys. To enable users to run data mining algorithms collaboratively in the cloud, we need an efficient scheme to process the encrypted data under multiple keys. Dot product is one of the most important building blocks of data mining algorithms. In this paper, we show how to give the cloud the permission to decrypt the encrypted dot product of two encrypted vectors without compromising the privacy of the data owners. We propose the first feasible scheme that trains a SVM (Support Vector Machine) classifier for both horizontally and vertically partitioned datasets using only one server. Existing schemes either can only handle a much simpler classifier (linear mean classifier) with two non-colluding servers or can only be applied to vertically partitioned dataset. We also show that our scheme not only preserves data privacy but also runs faster than existing schemes. © 2017 acm.

关键词： Cryptography

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Sound and Static Analysis of Session Fixation Vulnerabilities in PHP Web applications 17

Sound and Static Analysis of Session Fixation Vulnerabilitie...

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

作者： Amira, Abdelouahab Ouadjaout, Abdelraouf Derhab, Abdelouahid Badache, Nadjib CERIST Res Ctr Sci & Tech Informat Algiers Algeria A MIRA Univ Bejaia Algeria Univ Paris 06 LIP6 Paris France King Saud Univ Ctr Excellence Informat Assurance CoEIA Riyadh Saudi Arabia

ISBN: (纸本)9781450345231

Web applications use authentication mechanisms to provide user-friendly content to users. However, some dangerous techniques like session fixation attacks target these mechanisms, by making the legitimate user use a session identifier that is controlled by the attacker. In this way, he can then impersonate the legitimate user without the need to know his credentials. In this paper, we present SAWFIX, a PHP static analyzer that checks web applications for session fixation vulnerabilities. To the best of our knowledge, SAWFIX is the first analyzer that checks exhaustively for this type of vulnerabilities, while the other methods only ensure partial correctness that is limited to a fraction of possible executions. SAWFIX is based on abstract interpretation, which is a theory for approximating the semantics of programs and allows designing static analyzers that are fully automatic and sound by construction. We implemented a prototype of our approach and tested it on several complex web applications. We obtained promising results in terms of detection accuracy and processing time, which reflects the efficiency of our system.

关键词： Session fixation attacks Web application security Static program analysis Abstract interpretation

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Model-Based privacy and security Analysis with CARiSMA 2017

Model-Based Privacy and Security Analysis with CARiSMA

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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： Ahmadian, Amir Shayan Peldszus, Sven Ramadan, Qusai Juerjens, Jan Univ Koblenz Landau Mainz Germany

ISBN: (纸本)9781450351058

We present CARiSMA, a tool that is originally designed to support model-based security analysis of IT systems. In our recent work, we added several new functionalities to CARiSMA to support the privacy of personal data. Moreover, we introduced a mechanism to assist the system designers to perform a CARiSMA analysis by automatically initializing an appropriate CARiSMA analysis concerning security and privacy requirements. the motivation for our work is Article 25 of Regulation (EU) 2016/679, which requires appropriate technical and organizational controls must be implemented for ensuring that, by default, the processing of personal data complies with the principles on processing of personal data. this implies that initially IT systems must be analyzed to verify if such principles are respected. System models allow the system developers to handle the complexity of systems and to focus on key aspects such as privacy and security. CARiSMA is available at (http://***) and our screen cast at (https://***/b5zeHig3ARw).

关键词： System design analysis Model-based analysis privacy security

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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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Share a pie? privacy-Preserving Knowledge Base Export through Count-min Sketches 17

<i>Share a pie</i>? Privacy-Preserving Knowledge Base Export...

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

作者： Ucci, Daniele Aniello, Leonardo Baldoni, Roberto Univ Roma La Sapienza Res Ctr Cyber Intelligence & Informat Secur CIS Dept Comp Control & Management Engn Antonio Ruber Rome Italy

ISBN: (纸本)9781450345231

Knowledge base (KB) sharing among parties has been proven to be beneficial in several scenarios. However such sharing can arise considerable privacy concerns depending on the sensitivity of the information stored in each party's KB. In this paper, we focus on the problem of exporting a (part of a) KB of a party towards a receiving one. We introduce a novel solution that enables parties to export data in a privacy-preserving fashion, based on a probabilistic data structure, namely the count-min sketch. With this data structure, KBs can be exported in the form of key-value stores and inserted into a set of count-min sketches, where keys can be sensitive and values are counters. Count-min sketches can be tuned to achieve a given key collision probability, which enables a party to deny having certain keys in its own KB, and thus to preserve its privacy. We also introduce a metric, the gamma-deniability (novel for count-min sketches), to measure the privacy level obtainable with a count-min sketch. Furthermore, since the value associated to a key can expose to linkage attacks, noise can be added to a count-min sketch to ensure controlled error on retrieved values. Key collisions and noise alter the values contained in the exported KB, and can affect negatively the accuracy of a computation performed on the exported *** explore the tradeoff between privacy preservation and computation accuracy by experimental evaluations in two scenarios related to malware detection.

关键词： Information sharing knowledge base export privacy metric count-min sketches

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Graph Automorphism-Based, Semantics-Preserving security for the Resource Description Framework (RDF) 17

Graph Automorphism-Based, Semantics-Preserving Security for ...

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

作者： Lin, Zhiyuan Tripunitara, Mahesh Univ Waterloo ECE Waterloo ON Canada

ISBN: (纸本)9781450345231

We address security in the context of the Resource Description Framework (RDF), a graph-like data model for the web. One of RDF's compelling features is a precise, model-theoretic semantics. We first propose a threat model, and under it, observe that the technical challenge is really in hiding information that may be revealed by the structure of an RDF graph. We choose two quantitative, unconditional notions for securing graph-structure from the literature that address the threat model, and adapt them for RDF. We then consider the problem of devising algorithms for achieving a certain level of security while preserving the semantics of the input RDF graph. We observe that there are operations we can perform on an RDF graph that both provide such security and preserve semantics. We observe, further, that there is a natural way to quantify information-loss under these operations, and that there appears to be a natural trade-off between security and information-quality. We study this trade-off and establish fundamental results. We show that the RDF graphs that result from applying the operations induce a lattice that leads to a natural quantification of information-quality. We show also that achieving a certain level of security while retaining a certain level of information-quality is NP-complete under polynomialtime Turing reductions. Finally, towards an empirical assessment, we discuss our design and implementation of a reduction to CNF-SAT, and empirical results for two classes of RDF graphs. In summary, our work makes fundamental and practical contributions to semantics-preserving security for RDF.

关键词： unconditional security ISO computational complexity NP semantics graphs

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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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Achieving Differential privacy in Secure Multiparty data Aggregation Protocols on Star Networks 17

Achieving Differential Privacy in Secure Multiparty Data Agg...

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

作者： Bindschaedler, Vincent Rane, Shantanu Brito, Alejandro E. Rao, Vanishree Uzun, Ersin Univ Illinois Urbana IL 61801 USA Palo Alto Res Ctr Palo Alto CA USA

ISBN: (纸本)9781450345231

We consider the problem of privacy-preserving data aggregation in a star network topology, i.e., several untrusting participants connected to a single aggregator. We require that the participants do not discover each other's data, and the service provider remains oblivious to each participant's individual contribution. Furthermore, the final result is to be published in a differentially private manner, i.e., the result should not reveal the contribution of any single participant to a (possibly external) adversary who knows the contributions of all other participants. In other words, we require a secure multiparty computation protocol that also incorporates a differentially private mechanism. Previous solutions have resorted to caveats such as postulating a trusted dealer to distribute keys to the participants, or introducing additional entities to withhold the decryption key from the aggregator, or relaxing the star topology by allowing pairwise communication amongst the participants. In this paper, we show how to obtain a noisy (differentially private) aggregation result using Shamir secret sharing and additively homomorphic encryption without these mitigating assumptions. More importantly, while we assume semi-honest participants, we allow the aggregator to be stronger than semi-honest, specifically in the sense that he can try to reduce the noise in the differentially private result. To respect the differential privacy requirement, collusions of mutually untrusting entities need to be analyzed differently from traditional secure multiparty computation: It is not sufficient that such collusions do not reveal the data of honest participants;we must also ensure that the colluding entities cannot undermine differential privacy by reducing the amount of noise in the final result. Our protocols avoid this by requiring that no entity - neither the aggregator nor any participant - knows how much noise a participant contributes to the final result. We also ensure that if

关键词： secret sharing homomorphic encryption differential privacy

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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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application of the holistic data privacy and security Framework PaaSword 17

Application of the holistic Data Privacy and Security Framew...

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12th International conference on Availability, Reliability and security (ARES)

作者： Schork, Sebastian thomas Schwichtenberg, Antonia Alexakis, Spiros Moldovan, George CAS Software AG CAS Weg 1-5 D-76131 Karlsruhe Germany Siemens SRL Blvd Eroilor 3A Brasov 500007 Romania

ISBN: (纸本)9781450352574

Even though the adoption of cloud computing is accelerating, enterprises are still reluctant to use cloud-based services for processing sensible data, due to security and privacy concerns. the PaaSword framework provides functionalities for encrypted and distributed storage and context-aware access control, leveraging security and trust of cloud infrastructures and services and ensuring the protection, privacy and integrity of the data stored in the cloud. For proving its applicability, usability, effectiveness and value, the PaaSword consortium is currently developing and evaluating five pilot integration projects, which consist of typical scenarios specific to the particular user communities of which the pilot partners are part of, including PaaS providers, governmental institutions, logistics industry and providers of business applications. After a short overview of the framework, we present in detail expectations and integration experiences for two of the pilots: "Protection of personal data in a multi-tenant CRM environment" and "Secure sensors data fusion and analytics".

关键词： Cloud security data privacy PaaS SaaS Cryptography Context Awareness Context security Policy Engine Pilot Integration

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