Helmholtz Center for Information Security
CISPA – Helmholtz-Zentrum für InformationssicherheitNot a member yet
3406 research outputs found
Sort by
Parameterized Synthesis of Self-Stabilizing Protocols in Symmetric Rings
Get PDFSelf-stabilization in distributed systems is a technique to guarantee convergence to a set of legitimate states without external intervention when a transient fault or bad initialization occurs. Recently, there has been a surge of efforts in designing techniques for automated synthesis of self-stabilizing algorithms that are correct by construction. Most of these techniques, however, are not parameterized, meaning that they can only synthesize a solution for a fixed and predetermined number of processes. In this paper, we report a breakthrough in parameterized synthesis of self-stabilizing algorithms in symmetric rings. First, we develop tight cutoffs that guarantee (1) closure in legitimate states, and (2) deadlock-freedom outside the legitimates states. We also develop a sufficient condition for convergence in silent self-stabilizing systems. Since some of our cutoffs grow with the size of local state space of processes, we also present an automated technique that significantly increases the scalability of synthesis in symmetric networks. Our technique is based on SMT-solving and incorporates a loop of synthesis and verification guided by counterexamples. We have fully implemented our technique and successfully synthesized solutions to maximal matching, three coloring, and maximal independent set problems
Causal Inference on Multivariate and Mixed Type Data
Get PDFHow can we discover whether X causes Y , or vice versa,
that Y causes X, when we are only given a sample over their joint distribution? How can we do this such that X and Y can be univariate,
multivariate, or of different cardinalities? And, how can we do so regardless of whether X and Y are of the same, or of different data type, be it
discrete, numeric, or mixed? These are exactly the questions we answer.
We take an information theoretic approach, based on the Minimum Description Length principle, from which it follows that first describing the
data over cause and then that of effect given cause is shorter than the
reverse direction. Simply put, if Y can be explained more succinctly by
a set of classification or regression trees conditioned on X, than in the
opposite direction, we conclude that X causes Y . Empirical evaluation
on a wide range of data shows that our method, Crack, infers the correct causal direction reliably and with high accuracy on a wide range of
settings, outperforming the state of the art by a wide margi
Bounded Synthesis of Reactive Programs
No full textMost algorithms for the synthesis of reactive systems focus on the construction of finite-state machines rather than actual programs. This often leads to badly structured, unreadable code. In this paper, we present a bounded synthesis approach that automatically constructs, from a given specification in linear-time temporal logic (LTL), a program in Madhusudan's simple imperative language for reactive programs. We develop and compare two principal approaches for the reduction of the synthesis problem to a Boolean constraint satisfaction problem. The first reduction is based on a generalization of bounded synthesis to two-way alternating automata, the second reduction is based on a direct encoding of the program syntax in the constraint system. We report on preliminary experience with a prototype implementation, which indicates that the direct encoding outperforms the automata approach
MLCapsule: Guarded Offline Deployment of Machine Learning as a Service
No full textWith the widespread use of machine learning (ML) techniques, ML as a service has become increasingly popular. In this setting, an ML model resides on a server and users can query it with their data via an API. However, if the user's input is sensitive, sending it to the server is undesirable and sometimes even legally not possible. Equally, the service provider does not want to share the model by sending it to the client for protecting its intellectual property and pay-per-query business model.
In this paper, we propose MLCapsule, a guarded offline deployment of machine learning as a service. MLCapsule executes the model locally on the user's side and therefore the data never leaves the client. Meanwhile, MLCapsule offers the service provider the same level of control and security of its model as the commonly used server-side execution. In addition, MLCapsule is applicable to offline applications that require local execution. Beyond protecting against direct model access, we couple the secure offline deployment with defenses against advanced attacks on machine learning models such as model stealing, reverse engineering, and membership inference
DroidMate-2: A Platform for Android Test Generation
Get PDFAndroid 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-the-box 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%.
ACM DL Artifact: https://www.doi.org/10.1145/3264864.
For the details see:
https://github.com/uds-se/droidmate/wiki/ASE-2018:-Dat
Sicherheit und Datenschutz im Smart Grid – rechtliche Anforderungen und technische Lösungen
No full text
A Hybrid Model for Identity Obfuscation by Face Replacement
No full textAs more and more personal photos are shared and tagged in social media, avoiding privacy risks such as unintended recognition becomes increasingly challenging. We propose a new hybrid approach to obfuscate identities in photos by head replacement. Our approach combines state of the art parametric face synthesis with latest advances in Generative Adversarial Networks (GAN) for data-driven image synthesis. On the one hand, the parametric part of our method gives us control over the facial parameters and allows for explicit manipulation of the identity. On the other hand, the data-driven aspects allow for adding fine details and overall realism as well as seamless blending into the scene context. In our experiments, we show highly realistic output of our system that improves over the previous state of the art in obfuscation rate while preserving a higher similarity to the original image content
Diverse Conditional Image Generation by Stochastic Regression with Latent Drop-Out Codes
No full textRecent advances in Deep Learning and probabilistic modeling have led to strong improvements in generative models for images. On the one hand, Generative Adversarial Networks (GANs) have contributed a highly effective adversarial learning procedure, but still suffer from stability issues. On the other hand, Conditional Variational Auto-Encoders (CVAE) models provide a sound way of conditional modeling but suffer from mode-mixing issues. Therefore, recent work has turned back to simple and stable regression models that are effective at generation but give up on the sampling mechanism and the latent code representation. We propose a novel and efficient stochastic regression approach with latent drop-out codes that combines the merits of both lines of research. In addition, a new training objective increases coverage of the training distribution leading to improvements over the state of the art in terms of accuracy as well as diversity