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PRIMA: Privacy-Preserving Identity and Access Management at Internet-Scale
No full textThe management of identities on the Internet has evolved from the traditional approach (where each service provider stores and manages identities) to a federated identity management system (where the identity management is delegated to a set of identity providers). On the one hand, federated identity ensures usability and provides economic benefits to service providers. On the other hand, it poses serious privacy threats to users as well as service providers. The current technology, which is prevalently deployed on the Internet, allows identity providers to track the user's behavior across a broad range of services.
In this work, we propose PRIMA, a universal credential-based authentication system for supporting federated identity management in a privacy-preserving manner. Basically, PRIMA does not require any interaction between service providers and identity providers during the authentication process, thus preventing identity providers to profile users' behavior. Moreover, throughout the authentication process, PRIMA provides a mechanism for controlled disclosure of the users' private information. We have conducted comprehensive evaluations of the system to show the feasibility of our approach. Our performance analysis shows that an identity provider can process 1,426 to 3,332 requests per second when the key size is varied from 1024 to 2048-bit, respectively
Tagvisor: A Privacy Advisor for Sharing Hashtags
Get PDFHashtag has emerged as a widely used concept of popular culture
and campaigns, but its implications on people’s privacy have not
been investigated so far. In this paper, we present the first systematic
analysis of privacy issues induced by hashtags. We concentrate
in particular on location, which is recognized as one of the key
privacy concerns in the Internet era. By relying on a random forest
model, we show that we can infer a user’s precise location from
hashtags with accuracy of 70% to 76%, depending on the city. To
remedy this situation, we introduce a system called Tagvisor that
systematically suggests alternative hashtags if the user-selected
ones constitute a threat to location privacy. Tagvisor realizes this by
means of three conceptually different obfuscation techniques and a
semantics-based metric for measuring the consequent utility loss.
Our findings show that obfuscating as little as two hashtags already
provides a near-optimal trade-off between privacy and utility in our
dataset. This in particular renders Tagvisor highly time-efficient,
and thus, practical in real-world settings
Killing Three Birds with one Gaussian Process: Analyzing Attack Vectors on Classification
No full textThe wide usage of Machine Learning (ML) has lead to research on the attack vectors and vulnerability of these
systems. The defenses in this area are however still an open
problem, and often lead to an arms race.
We define a naive, secure classifier at test time and show
that a Gaussian Process (GP) is an instance of this classifier given two assumptions: one concerns the distances in the training data, the other rejection at test time. Using these assumptions, we are able to show that a classifier is either secure, or generalizes and thus learns.
Our analysis also points towards another factor influencing
robustness, the curvature of the classifier. This connection is not unknown for linear models, but GP offer an ideal framework to study this relationship for nonlinear classifiers. We evaluate on five security and two computer vision datasets applying test and training time attacks and membership inference. We show that we only change which attacks are needed to succeed, instead of alleviating the threat. Only for membership inference, there is a setting in which attacks are unsuccessful (<10% increase in accuracy over random guess).
Given these results, we define a classification scheme based
on voting, ParGP. This allows us to decide how many points
vote and how large the agreement on a class has to be. This
ensures a classification output only in cases when there is
evidence for a decision, where evidence is parametrized. We
evaluate this scheme and obtain promising results
Didn’t You Hear Me? — Towards More Successful Web Vulnerability Notifications
Get PDFAfter treating the notification of affected parties as mere side-notes in research, our community has recently put more focus on how vulnerability disclosure can be conducted at scale. The first works in this area have shown that while notifications are helpful to a significant fraction of operators, the vast majority of systems remain unpatched. In this paper, we build on these previous works, aiming to understand why the effects are not more significant. To that end, we report on a notification experiment targeting more than 24,000 domains, which allowed us to analyze what technical and human aspects are roadblocks to a successful campaign. As part of this experiment, we explored potential alternative notification channels beyond email, including social media and phone. In addition, we conducted an anonymous survey with the notified operators, investigating their perspectives on our notifications. We show the pitfalls of email-based communications, such as the impact of anti-spam filters, the lack of trust by recipients, and hesitations to fix vulnerabilities despite awareness. However, our exploration of alternative communication channels did not suggest a more promising medium. Seeing these results, we pinpoint future directions in improving security notifications
IT-Forscher als potentielle Straftäter? – IT-Sicherheitsforschung zwischen Wissenschaftsfreiheit und Strafrecht
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DroidMate-2: A Platform for Android Test Generation
No full textAndroid applications (apps) represent an ever increasing portion of the software market. Automated test input generators are the state of the art for testing and security analysis. We introduce DroidMate-2 (DM-2), a platform to easily assist both developers and researchers to customize, develop and test new test generators. DM-2 can be used without app instrumentation or operating system modifications, as a test generator on real devices and emulators for app testing or regression testing. Additionally, it provides sensitive resource monitoring or blocking capabilities through a lightweight app instrumentation, out-of-thebox statement coverage measurement through a fully-fledged app instrumentation and native experiment reproducibility. In our experiments we compared DM-2 against DroidBot, a state-of-the-art test generator by measuring statement coverage. Our results show that DM-2 reached 96% of its peak coverage in less than 2/3 of the time needed by DroidBot, allowing for better and more efficient tests. On short runs (5 minutes) DM-2 outperformed DroidBot by 7% while in longer runs (1 hour) this difference increases to 8%