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Panel: Trustworthy data Science 17

Panel: Trustworthy Data Science

引用

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

作者： Doupe, Adam Arizona State Univ Tempe AZ 85287 USA

ISBN: (纸本)9781450345231

Much of the research that our community publishes is based on data. However, an open question remains: are the results of data science trustworthy, and how can we increase our trust in data science? Accomplishing this goal is difficult, as we must trust the inputs, systems, and results of data science. This panel will discuss the current state of trustworthy data science, and explore possible technical, legal, and cultural solutions that can increase our trust in the input, systems, and results of data science.

关键词： data Science Panel Trust

来源：评论

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Towards Practical privacy-Preserving Life Cycle Assessment Computations 17

Towards Practical Privacy-Preserving Life Cycle Assessment C...

引用

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

作者： Sahin, Cetin Kuczenski, Brandon Egecioglu, Omer El Abbadi, Amr Univ Calif Santa Barbara Santa Barbara CA 93106 USA

ISBN: (纸本)9781450345231

Life Cycle Assessment(LCA) is crucial for evaluating the ecological sustainability of a product or service, and the accurate evaluation of sustainability requires detailed and transparent information about industrial activities. However, such information is usually considered confidential and withheld from the public. In this paper, we present a study of privacy in the context of LCA. The main goal is to explore the privacy challenges in sustainability assessment considering the protection of trade secrets while increasing transparency of industrial activities. To overcome privacy concerns, we apply differential privacy to LCA computations considering the idiosyncratic features of LCA data. Our assessments on a specific real-life example show that it is possible to achieve privacy-preserving LCA computations without losing the utility of data completely.

关键词： differential privacy privacy-preserving data publication life cycle assessment LCA differentially private matrix factorization

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Aegis: Automatic Enforcement of security Policies in Workflow-driven Web applications 17

Aegis: Automatic Enforcement of Security Policies in Workflo...

引用

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

作者： Compagna, Luca dos Santos, Daniel R. Ponta, Serena Elisa Ranise, Silvio SAP Labs France Mougins France Fdn Bruno Kessler Trento Italy Univ Trento Trento Italy

ISBN: (纸本)9781450345231

Organizations often expose business processes and services as web applications. Improper enforcement of security policies in these applications leads to business logic vulnerabilities that are hard to find and may have dramatic security implications. Aegis is a tool to automatically synthesize run-time monitors to enforce control-flow and data-flow integrity, as well as authorization policies and constraints in web applications. The enforcement of these properties can mitigate attacks, e.g., authorization bypass and workflow violations, while allowing regulatory compliance in the form of, e.g., Separation of Duty. Aegis is capable of guaranteeing business continuity while enforcing the security policies. We evaluate Aegis on a set of real-world applications, assessing the enforcement of policies, mitigation of vulnerabilities, and performance overhead.

关键词： Web application Policy Enforcement Workflow Satisfiability

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Efficient Commodity Matching for privacy-Preserving Two-Party Bartering 17

Efficient Commodity Matching for Privacy-Preserving Two-Part...

引用

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

作者： Forg, Fabian Wetzel, Susanne Meyer, Ulrike Stevens Inst Technol Hoboken NJ 07030 USA Rhein Westfal TH Aachen Aachen Germany

ISBN: (纸本)9781450345231

Current bartering platforms place the burden of finding simultaneously executable quotes on their users. In addition, these bartering platforms do not keep quotes private. To address these shortcomings, this paper introduces a privacy-preserving bartering protocol secure in the semi-honest model. At its core, the novel bartering protocol uses a newly-developed bipartite matching protocol which determines simultaneously executable quotes in an efficient manner. While the new privacy-preserving bipartite matching protocol does not always yield the maximal set of simultaneously executable quotes, it keeps the parties' quotes private at all times. Moreover, our new privacy-preserving bipartite matching protocol is more efficient than existing solutions in that it only requires linear communication in the number of quotes the parties specify.

关键词： secure multi-party computation bartering semi-honest model bipartite matching set intersection privacy-preserving

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Differentially-Private Big data Analytics for High-Speed Research Network Traffic Measurement 17

Differentially-Private Big Data Analytics for High-Speed Res...

引用

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

作者： Niculaescu, Oana-Georgiana Maruseac, Mihai Ghinita, Gabriel UMass Boston Boston MA 02125 USA

ISBN: (纸本)9781450345231

High-speed research networks (e.g., Internet2, Geant) represent the backbone of large-scale research projects that bring together stakeholders from academia, industry and government. Such projects have increasing demands on throughput (e.g., 100Gbps line rates), and require a high amount of configurability. Collecting and sharing traffic data for such networks can help in detecting hotspots, troubleshooting, and designing novel routing protocols. However, sharing network data directly introduces serious privacy breaches, as an adversary may be able to derive private details about individual users (e.g., personal preferences or activity patterns). Our objective is to sanitize high-speed research network data according to the de-facto standard of differential privacy (DP), thus supporting benefic applications of traffic measurement without compromising individuals' privacy. In this paper, we present an initial framework for computing DP-compliant big data analytics for high-speed research network data. Specifically, we focus on sharing data at flow-level granularity, and we describe our initial steps towards an environment that relies on Hadoop and HBase to support privacy-preserving NetFlow analytics.

关键词： network measurement differential privacy

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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...

引用

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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Identifying HTTPS-Protected Netflix Videos in Real-Time 17

Identifying HTTPS-Protected Netflix Videos in Real-Time

引用

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

作者： Reed, Andrew Kranch, Michael US Mil Acad Dept Elect Engn & Comp Sci West Point NY 10996 USA

ISBN: (纸本)9781450345231

After more than a year of research and development, Netflix recently upgraded their infrastructure to provide HTTPS encryption of video streams in order to protect the privacy of their viewers. Despite this upgrade, we demonstrate that it is possible to accurately identify Netflix videos from passive traffic capture in real-time with very limited hardware requirements. Specifically, we developed a system that can report the Netflix video being delivered by a TCP connection using only the information provided by TCP/IP headers. To support our analysis, we created a fingerprint database comprised of 42,027 Netflix videos. Given this collection of fingerprints, we show that our system can differentiate between videos with greater than 99.99% accuracy. Moreover, when tested against 200 random 20-minute video streams, our system identified 99.5% of the videos with the majority of the identifications occurring less than two and a half minutes into the video stream.

关键词： privacy traffic analysis dynamic adaptive streaming over HTTP Netflix

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Detecting Mobile application Spoofing Attacks by Leveraging User Visual Similarity Perception 17

Detecting Mobile Application Spoofing Attacks by Leveraging ...

引用

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

作者： Malisa, Luka Kostiainen, Kari Capkun, Srdjan Swiss Fed Inst Technol Inst Informat Secur Zurich Switzerland

ISBN: (纸本)9781450345231

Mobile application spoofing is an attack where a malicious mobile app mimics the visual appearance of another one. A common example of mobile application spoofing is a phishing attack where the adversary tricks the user into revealing her password to a malicious app that resembles the legitimate one. In this paper, we propose a novel spoofing detection approach, tailored to the protection of mobile app login screens, using screenshot extraction and visual similarity comparison. We use deception rate as a novel similarity metric for measuring how likely the user is to consider a potential spoofing app as one of the protected applications. We conducted a large-scale online study where participants evaluated spoofing samples of popular mobile app login screens, and used the study results to implement a detection system that accurately estimates deception rate. We show that efficient detection is possible with low overhead.

关键词： spoofing visual similarity malware detection phishing

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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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privacy-Preserving HMM Forward Computation 17

Privacy-Preserving HMM Forward Computation

引用

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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