Helmholtz Center for Information Security
CISPA – Helmholtz-Zentrum für InformationssicherheitNot a member yet
3406 research outputs found
Sort by
Lord of the x86 Rings: A Portable User Mode Privilege Separation Architecture on x86
Get PDFModern applications often involve processing of sensitive information. However, the lack of privilege separation within the user space leaves sensitive application secret such as cryptographic keys just as unprotected as a "hello world" string. Cutting-edge hardware-supported security features are being introduced. However, the features are often vendor-specific or lack compatibility with older generations of the processors. The situation leaves developers with no portable solution to incorporate protection for the sensitive application component.
We propose LOTRx86, a fundamental and portable approach for user-space privilege separation. Our approach creates a more privileged user execution layer called PrivUser by harnessing the underused intermediate privilege levels on the x86 architecture. The PrivUser memory space, a set of pages within process address space that are inaccessible to user mode, is a safe place for application secrets and routines that access them. We implement the LOTRx86 ABI that exports the privcall interface to users to invoke secret handling routines in PrivUser. This way, sensitive application operations that involve the secrets are performed in a strictly controlled manner. The memory access control in our architecture is privilege-based, accessing the protected application secret only requires a change in the privilege, eliminating the need for costly remote procedure calls or change in address space. We evaluated our platform by developing a proof-of-concept LOTRx86-enabled web server that employs our architecture to securely access its private key during an SSL connection. We conducted a set of experiments including a performance measurement on the PoC on both Intel and AMD PCs, and confirmed that LOTRx86 incurs only a limited performance overhead
Dissecting Privacy Risks in Biomedical Data
Get PDFThe decreasing costs of molecular profiling has
fueled the biomedical research community with a plethora
of new types of biomedical data, enabling a breakthrough
towards a more precise and personalized medicine. However,
the release of these intrinsically highly sensitive data poses
a new severe privacy threat. While biomedical data is largely
associated with our health, there also exist various correlations
between different types of biomedical data, along the temporal
dimension, and also in-between family members. However,
so far, the security community has focused on privacy risks
stemming from genomic data, largely overlooking the manifold
interdependencies between other biomedical data.
In this paper, we present a generic framework for quantifying the privacy risks in biomedical data taking into account the
various interdependencies between data (i) of different types,
(ii) from different individuals, and (iii) at different time. To this
end, we rely on a Bayesian network model that allows us to take
all aforementioned dependencies into account and run exact
probabilistic inference attacks very efficiently. Furthermore,
we introduce a generic algorithm for building the Bayesian
network, which encompasses expert knowledge for known
dependencies, such as genetic inheritance laws, and learns
previously unknown dependencies from the data. Then, we
conduct a thorough inference risk evaluation with a very rich
dataset containing genomic and epigenomic data of mothers
and children over multiple years. Besides effective probabilistic
inference, we further demonstrate that our Bayesian network
model can also serve as a building block for other attacks. We
show that, with our framework, an adversary can efficiently
identify the parent-child relationships based on methylation
data with a success rate of 95%
The Battle Of The Attack Detection Algorithms: Disclosing Cyber Attacks On Water Distribution Networks
No full textAdversarial Scene Editing: Automatic Object Removal from Weak Supervision
No full textWhile great progress has been made recently in automatic image manipulation, it has been limited to object centric images like faces or structured scene datasets. In this work, we take a step towards general scene-level image editing by developing an automatic interaction-free object removal model. Our model learns to find and remove objects from general scene images using image-level labels and unpaired data in a generative adversarial network (GAN) framework. We achieve this with two key contributions: a two-stage editor architecture consisting of a mask generator and image in-painter that co-operate to remove objects, and a novel GAN based prior for the mask generator that allows us to flexibly incorporate knowledge about object shapes. We experimentally show on two datasets that our method effectively removes a wide variety of objects using weak supervision only
Origo: Causal Inference by Compression
Get PDFCausal inference from observational data is one of the most fundamental problems in science. In general, the task is to tell whether it is more likely that X caused Y, or vice versa, given only data over their joint distribution. In this paper we propose a general inference framework based on Kolmogorov complexity, as well as a practical and computable instantiation based on the Minimum Description Length (MDL) principle.
Simply put, we propose causal inference by compression. That is, we infer that X is a likely cause of Y if we can better compress the data by first encoding X, and then encoding Y given X, than in the other direction. To show this works in practice, we propose Origo, an efficient method for inferring the causal direction from binary data. Origo employs the lossless Pack compressor (Tatti & Vreeken, 2008) and searches for that set of decision trees that encodes the data most succinctly. Importantly, it works directly on the data and does not require assumptions about neither distributions nor the type of causal relations.
To evaluate Origo in practice, we provide extensive experiments on synthetic, benchmark, and real-world data, including three case studies. Altogether the experiments show that Origo reliably infers the correct causal direction on a wide range of settings
Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms
Get PDFThe reliable fraction of information is an attractive score for quantifying (functional) dependencies in high-dimensional data. In this paper, we systematically explore the algorithmic implications of using this measure for optimization. We show that the problem is NP-hard, which justifies the usage of worst-case exponential-time as well as heuristic search methods. We then substantially improve the practical performance for both optimization styles by deriving a novel admissible bounding function that has an unbounded potential for additional pruning over the previously proposed one. Finally, we empirically investigate the approximation ratio of the greedy algorithm and show that it produces highly competitive results in a fraction of time needed for complete branch-and-bound style search
Practical Test Dependency Detection
Get PDFRegression tests should consistently produce the same outcome when executed against the same version of the system under test. Recent studies, however, show a different picture: in many cases simply changing the order in which tests execute is enough to produce different test outcomes. These studies also identify the presence of dependencies between tests as one likely cause of this behavior. Test dependencies affect the quality of tests and of the correlated development activities, like regression test selection, prioritization, and parallelization, which assume that tests are independent. Therefore, developers must promptly identify and resolve problematic test dependencies.
This paper presents PRADET, a novel approach for detecting problematic dependencies that is both effective and efficient. PRADET uses a systematic, data-driven process to detect problematic test dependencies significantly faster and more precisely than prior work. PRADET scales to analyze large projects with thousands of tests that existing tools cannot analyze in reasonable amount of time, and found 27 previously unknown dependencies
