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privacy-Preserving HMM Forward Computation 17

Privacy-Preserving HMM Forward Computation

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

作者： Ziegeldorf, Jan Henrik Metzke, Jan Rueth, Jan Henze, Martin Wehrle, Klaus Rhein Westfal TH Aachen Commun & Distributed Syst COMSYS Aachen Germany

ISBN: (纸本)9781450345231

In many areas such as bioinformatics, pattern recognition, and signal processing, Hidden Markov Models (HMMs) have become an indispensable statistical tool. A fundamental building block for these applications is the Forward algorithm which computes the likelihood to observe a given sequence of emissions for a given HMM. the classical Forward algorithm requires that one party holds both the model and observation sequences. However, we observe for many emerging applications and services that the models and observation sequences are held by different parties who are not able to share their information due to applicable data protection legislation or due to concerns over intellectual property and privacy. this renders the application of HMMs infeasible. In this paper, we show how to resolve this evident conflict of interests using secure two-party computation. Concretely, we propose Priward which enables two mutually untrusting parties to compute the Forward algorithm securely, i.e., without requiring either party to share her sensitive inputs with the other or any third party. the evaluation of our implementation of Priward shows that our solution is efficient, accurate, and outperforms related works by a factor of 4 to 126. To highlight the applicability of our approach in real-world deployments, we combine Priward with the widely used HMMER biosequence analysis framework and show how to analyze real genome sequences in a privacy-preserving manner.

关键词： garbled circuits secure two-party computation hidden Markov models forward algorithm privacy-preserving protocols

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A Study of security Vulnerabilities on Docker Hub 17

A Study of Security Vulnerabilities on Docker Hub

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

作者： Shu, Rui Gu, Xiaohui Enck, William North Carolina State Univ Raleigh NC 27695 USA

ISBN: (纸本)9781450345231

Docker containers have recently become a popular approach to provision multiple applications over shared physical hosts in a more lightweight fashion than traditional virtual machines. this popularity has led to the creation of the Docker Hub registry, which distributes a large number of official and community images. In this paper, we study the state of security vulnerabilities in Docker Hub images. We create a scalable Docker image vulnerability analysis (DIVA) framework that automatically discovers, downloads, and analyzes both official and community images on Docker Hub. Using our framework, we have studied 356,218 images and made the following findings: (1) both official and community images contain more than 180 vulnerabilities on average when considering all versions;(2) many images have not been updated for hundreds of days;and (3) vulnerabilities commonly propagate from parent images to child images. these findings demonstrate a strong need for more automated and systematic methods of applying security updates to Docker images and our current Docker image analysis framework provides a good foundation for such automatic security update.

关键词： Docker Images security Vulnerabilities Vulnerability Propagation

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Discovering Browser Extensions via Web Accessible Resources 17

Discovering Browser Extensions via Web Accessible Resources

引用

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

作者： Sjosten, Alexander Van Acker, Steven Sabelfeld, Andrei Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781450345231

Browser extensions provide a powerful platform to enrich browsing experience. At the same time, they raise important security questions. From the point of view of a website, some browser extensions are invasive, removing intended features and adding unintended ones, e.g. extensions that hijack Facebook likes. Conversely, from the point of view of extensions, some websites are invasive, e.g. websites that bypass ad blockers. Motivated by security goals at clash, this paper explores browser extension discovery, through a non-behavioral technique, based on detecting extensions' web accessible resources. We report on an empirical study with free Chrome and Firefox extensions, being able to detect over 50% of the top 1,000 free Chrome extensions, including popular security- and privacy-critical extensions such as AdBlock, LastPass, Avast Online security, and Ghostery. We also conduct an empirical study of non-behavioral extension detection on the Alexa top 100,000 websites. We present the dual measures of making extension detection easier in the interest of websites and making extension detection more difficult in the interest of extensions. Finally, we discuss a browser architecture that allows a user to take control in arbitrating the conflicting security goals.

关键词： Web security Browser extensions Large-scale study

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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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DroidSieve: Fast and Accurate Classification of Obfuscated Android Malware 17

DroidSieve: Fast and Accurate Classification of Obfuscated A...

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

作者： Suarez-Tangil, Guillermo Dash, Santanu Kumar Ahmadi, Mansour Kinder, Johannes Giacinto, Giorgio Cavallaro, Lorenzo Royal Holloway Univ London London England Univ Cagliari Cagliari Italy

ISBN: (纸本)9781450345231

With more than two million applications, Android marketplaces require automatic and scalable methods to efficiently vet apps for the absence of malicious threats. Recent techniques have successfully relied on the extraction of lightweight syntactic features suitable for machine learning classification, but despite their promising results, the very nature of such features suggest they would unlikely-on their own-be suitable for detecting obfuscated Android malware. To address this challenge, we propose DroidSieve, an Android malware classifier based on static analysis that is fast, accurate, and resilient to obfuscation. For a given app, DroidSieve first decides whether the app is malicious and, if so, classifies it as belonging to a family of related malware. DroidSieve exploits obfuscation-invariant features and artifacts introduced by obfuscation mechanisms used in malware. At the same time, these purely static features are designed for processing at scale and can be extracted quickly. For malware detection, we achieve up to 99.82% accuracy with zero false positives;for family identification of obfuscated malware, we achieve 99.26% accuracy at a fraction of the computational cost of state-of-the-art techniques.

关键词： Android Malware Detection Malware Family Identification Obfuscation Native Code security Machine Learning Classification Scalability

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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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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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Prioritized Analysis of Inter-App Communication Risks 17

Prioritized Analysis of Inter-App Communication Risks

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

作者： Liu, Fang Cai, Haipeng Wang, Gang Yao, Danfeng (Daphne) Elish, Karim O. Ryder, Barbara G. Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA Florida Polytech Univ Dept Comp Sci Lakeland FL USA

ISBN: (纸本)9781450345231

Inter-Component Communication (ICC) enables useful interactions between mobile apps. However, misuse of ICC exposes users to serious threats such as intent hijacking/spoofing and app collusions, allowing malicious apps to access privileged user data via another app. Unfortunately, existing ICC analyses are largely incompetent in both accuracy and scale. this poster points out the need and technical challenges of prioritized analysis of inter-app ICC risks. We propose MR-Droid, a MapReduce-based computing framework for accurate and scalable inter-app ICC analysis in Android. MR-Droid extracts data-flow features between multiple communicating apps and the target apps to build a large-scale ICC graph. Our approach is to leverage the ICC graph to provide contexts for inter-app communications to produce precise alerts and prioritize risk assessments. this process requires large app-pair data, which is enabled by our MapReduce-based program analysis. Our initial extensive experiments on 11,996 apps from 24 app categories (13 million pairs) demonstrate the scalability of our approach.

关键词： hijacking spoofing android security MapReduce ICC risk analysis

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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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"If You Can't Beat them, Join them": A Usability Approach to Interdependent privacy in Cloud Apps 17

"If You Can't Beat them, Join them": A Usability Approach to...

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

作者： Harkous, Hamza Aberer, Karl Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9781450345231

Cloud storage services, like Dropbox and Google Drive, have growing ecosystems of 3rd party apps that are designed to work with users' cloud files. Such apps often request full access to users' files, including files shared with collaborators. Hence, whenever a user grants access to a new vendor, she is inflicting a privacy loss on herself and on her collaborators too. Based on analyzing a real dataset of 183 Google Drive users and 131 third party apps, we discover that collaborators inflict a privacy loss which is at least 39% higher than what users themselves cause. We take a step toward minimizing this loss by introducing the concept of History-based decisions. Simply put, users are informed at decision time about the vendors which have been previously granted access to their data. thus, they can reduce their privacy loss by not installing apps from new vendors whenever possible. Next, we realize this concept by introducing a new privacy indicator, which can be integrated within the cloud apps' authorization interface. Via a web experiment with 141 participants recruited from CrowdFlower, we show that our privacy indicator can significantly increase the user's likelihood of choosing the app that minimizes her privacy loss. Finally, we explore the network effect of History-based decisions via a simulation on top of large collaboration networks. We demonstrate that adopting such a decision-making process is capable of reducing the growth of user privacy loss by 40% in a Google Drive-based network and by 70% in an author collaboration network. this is despite the fact that we neither assume that users cooperate nor that they exhibit altruistic behavior. To our knowledge, our work is the first to provide quantifiable evidence of the privacy risk that collaborators pose in cloud apps. We are also the first to mitigate this problem via a usable privacy approach.

关键词： interdependent privacy 3rd party apps privacy cloud computing usability google drive DropBox

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