| 1. |
- Jin, Hongyu, et al.
(author)
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Resilient privacy protection for location-based services through decentralization
- 2019
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In: ACM Transactions on Privacy and Security. - : Association for Computing Machinery. - 2471-2566 .- 2471-2574. ; 22:4
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Journal article (peer-reviewed)abstract
- Location-Based Services (LBSs) provide valuable services, with convenient features for mobile users. However, the location and other information disclosed through each query to the LBS erodes user privacy. This is a concern especially because LBS providers can be honest-but-curious, collecting queries and tracking users' whereabouts and infer sensitive user data. This motivated both centralized and decentralized location privacy protection schemes for LBSs: anonymizing and obfuscating LBS queries to not disclose exact information, while still getting useful responses. Decentralized schemes overcome disadvantages of centralized schemes, eliminating anonymizers, and enhancing users' control over sensitive information. However, an insecure decentralized system could create serious risks beyond private information leakage. More so, attacking an improperly designed decentralized LBS privacy protection scheme could be an effective and low-cost step to breach user privacy. We address exactly this problem, by proposing security enhancements for mobile data sharing systems. We protect user privacy while preserving accountability of user activities, leveraging pseudonymous authentication with mainstream cryptography. We show our scheme can be deployed with off-the-shelf devices based on an experimental evaluation of an implementation in a static automotive testbed.
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| 2. |
- Balliu, Musard, et al.
(author)
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Friendly Fire : Cross-App Interactions in IoT Platforms
- 2021
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In: ACM Transactions on Privacy and Security (TOPS). - : Association for Computing Machinery (ACM). - 2471-2566 .- 2471-2574. ; 24:3, s. 1-40
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Journal article (peer-reviewed)abstract
- IoT platforms enable users to connect various smart devices and online services via reactive apps running onthe cloud. These apps, often developed by third-parties, perform simple computations on data triggered byexternal information sources and actuate the results of computations on external information sinks. Recentresearch shows that unintended or malicious interactions between the different (even benign) apps of a usercan cause severe security and safety risks. These works leverage program analysis techniques to build toolsfor unveiling unexpected interference across apps for specific use cases. Despite these initial efforts, we arestill lacking a semantic framework for understanding interactions between IoT apps. The question of whatsecurity policy cross-app interference embodies remains largely unexplored.This paper proposes a semantic framework capturing the essence of cross-app interactions in IoT platforms.The framework generalizes and connects syntactic enforcement mechanisms to bisimulation-based notionsof security, thus providing a baseline for formulating soundness criteria of these enforcement mechanisms.Specifically, we present a calculus that models the behavioral semantics of a system of apps executingconcurrently, and use it to define desirable semantic policies targeting the security and safety of IoT apps.To demonstrate the usefulness of our framework, we define and implement static analyses for enforcingcross-app security and safety, and prove them sound with respect to our semantic conditions. We also leveragereal-world apps to validate the practical benefits of our tools based on the proposed enforcement mechanisms.
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| 3. |
- Cordero, Carlos Garcia, et al.
(author)
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On Generating Network Traffic Datasets with Synthetic Attacks for Intrusion Detection
- 2021
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In: ACM TRANSACTIONS ON PRIVACY AND SECURITY. - : ASSOC COMPUTING MACHINERY. - 2471-2566 .- 2471-2574. ; 24:2
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Journal article (peer-reviewed)abstract
- Most research in the field of network intrusion detection heavily relies on datasets. Datasets in this field, however, are scarce and difficult to reproduce. To compare, evaluate, and test related work, researchers usually need the same datasets or at least datasets with similar characteristics as the ones used in related work. In this work, we present concepts and the Intrusion Detection Dataset Toolkit (ID2T) to alleviate the problem of reproducing datasets with desired characteristics to enable an accurate replication of scientific results. Intrusion Detection Dataset Toolkit (ID2T) facilitates the creation of labeled datasets by injecting synthetic attacks into background traffic. The injected synthetic attacks created by ID2T blend with the background traffic by mimicking the background traffics properties. This article has three core contributions. First, we present a comprehensive survey on intrusion detection datasets. In the survey, we propose a classification to group the negative qualities found in the datasets. Second, the architecture of ID2T is revised, improved, arid expanded in comparison to previous work. The architectural changes enable ID2T to inject recent and advanced attacks, such as the EternalBlue exploit or a peer-to-peer botnet. ID2Ts functionality provides a set of tests, known as TIDED, that helps identify potential defects in the background traffic into which attacks are injected. Third, we illustrate how ID2T is used in different use-case scenarios to replicate scientific results with the help of reproducible datasets. ID2T is open source software and is made available to the community to expand its arsenal of attacks and capabilities.
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| 4. |
- Engström, Viktor, et al.
(author)
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Automated Security Assessments of Amazon Web Service Environments
- 2022
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In: ACM Transactions on Privacy and Security. - : Association for Computing Machinery (ACM). - 2471-2566 .- 2471-2574. ; 26:2, s. 1-31
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Journal article (peer-reviewed)abstract
- Migrating enterprises and business capabilities to cloud platforms like Amazon Web Services (AWS) has become increasingly common. However, securing cloud operations, especially at large scales, can quickly become intractable. Customer-side issues such as service misconfigurations, data breaches, and insecure changes are prevalent. Furthermore, cloud-specific tactics and techniques paired with application vulnerabilities create a large and complex search space. Various solutions and modeling languages for cloud security assessments exist. However, no single one appeared sufficiently cloud-centered and holistic. Many also did not account for tactical security dimensions. This article, therefore, presents a domain-specific modeling language for AWS environments. When used to model AWS environments, manually or automatically, the language automatically constructs and traverses attack graphs to assess security. Assessments, therefore, require minimal security expertise from the user. The modeling language was primarily tested on four third-party AWS environments through securiCAD Vanguard, a commercial tool built around the AWS modeling language. The language was validated further by measuring performance on models provided by anonymous end users and a comparison with a similar open source assessment tool. As of March 2020, the modeling language could represent essential AWS structures, cloud tactics, and threats. However, the tests highlighted certain shortcomings. Data collection steps, such as planted credentials, and some missing tactics were obvious. Nevertheless, the issues covered by the DSL were already reminiscent of common issues with real-world precedents. Future additions to attacker tactics and addressing data collection should yield considerable improvements.
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| 5. |
- Karegar, Farzaneh, et al.
(author)
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The Dilemma of User Engagement in Privacy Notices : Effects of Interaction Modes and Habituation on User Attention
- 2020
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In: ACM Transactions and Security. - : ACM Digital Library. - 2471-2566 .- 2471-2574. ; 23:1, s. 1-38
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Journal article (peer-reviewed)abstract
- Privacy notices and consent forms are the means of conveying privacy policy information to users. In Europe, a valid consent needs to be confirmed by a clear affirmative action. Despite previous research, it is not yet clear whether user engagement with consent forms via different types of interactions for confirming consent may play a significant role in effectively drawing user attention to the content, even after repeated exposure. We investigate, in a laboratory study, how different types of interactions that engage users with consent forms differ in terms of their effectiveness, efficiency, and user satisfaction. In addition, we examine if and how habituation affects user attention and satisfaction, and the time they spend on giving their consent. We conducted a controlled experiment with 80 participants in four different groups where people either were engaged actively with the policy content via Drag and Drop (DAD), Swipe, or Checkboxes, or were not actively engaged with the content (as the control condition) in a first-exposure phase and in a habituation phase. We measured user attention to consent forms along multiple dimensions, including direct, objective measurements and indirect, self-reported measures. Our results show that the different types of interactionsmay affect user attention to certain parts of policy information. In particular, the DAD action results in significantly more user attention to the data items compared to other groups. However, with repeated exposure to consent forms, the difference disappears. We conclude that user engagement with policy content needs to be designed with care, so that attention to substantial policy information is increased and not negatively affected.
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