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Testing Apps With Real-World Inputs
Get PDFTo test mobile apps, one requires realistic and coherent test inputs. The Link approach for Web testing has shown that knowledge bases such as DBPedia can be a reliable source of semantically coherent inputs. In this paper, we adapt and extend the Link approach towards test generation for mobile applications:
(1) We identify and match descriptive labels with input fields, based on the Gestalt principles of human perception;
(2) We then use natural language processing techniques to extract the concept associated with the label;
(3) We use this concept to query a knowledge base for candidate input values;
(4) We cluster the UI elements according to their functionality into input and actions, filling the input elements first and then interacting with the actions.
Our evaluation shows that leveraging knowledge bases for testing mobile apps with realistic inputs is effective. On average, our approach covered 9% more statements than randomly generated text inputs
Two-Round Oblivious Transfer from CDH or LPN
Get PDFWe show a new general approach for constructing maliciously-secure two-round oblivious
transfer (OT). Specifically, we provide a generic sequence of transformations to upgrade a very
basic notion of two-round OT, which we call elementary OT, to UC-secure OT. We then give
simple constructions of elementary OT under the Computational Diffie-Hellman (CDH) assumption or the Learning Parity with Noise (LPN) assumption, yielding the first constructions of
malicious (UC-secure) two-round OT under these assumptions. Since two-round OT is complete
for two-round 2-party and multi-party computation in the malicious setting, we also achieve the
first constructions of the latter under these assumptions
Hardness of LWE on General Entropic Distribution
Get PDFThe hardness of the Learning with Errors (LWE) problem is by now a cornerstone of the cryptographic landscape. In many of its applications the so called ``LWE secret'' is not sampled uniformly, but comes from a distribution with some min-entropy. This variant, known as ``Entropic LWE'', has been studied in a number of works, starting with Goldwasser et al. (ICS 2010). However, so far it was only known how to prove the hardness of Entropic LWE for secret distributions supported inside a ball of small radius.
In this work we resolve the hardness of Entropic LWE with arbitrary long secrets, in the following sense. We show an entropy bound that guarantees the security of arbitrary Entropic LWE. This bound is higher than what is required in the ball-bounded setting, but we show that this is essentially tight. Tightness is shown unconditionally for highly-composite moduli, and using black-box impossibility for arbitrary moduli.
Technically, we show that the entropic hardness of LWE relies on a simple to describe lossiness property of the distribution of secrets itself. This is simply the probability of recovering a random sample from this distribution s, given s+e, where e is Gaussian noise (i.e. the quality of the distribution of secrets as an error correcting code for Gaussian noise). We hope that this characterization will make it easier to derive entropic LWE results more easily in the future. We also use our techniques to show new results for the ball-bounded setting, essentially showing that under a strong enough assumption even polylogarithmic entropy suffices
Semantic Bottlenecks: Quantifying and Improving Inspectability of Deep Representations
No full textToday’s deep learning systems deliver high performance based on end-to-end training but are notoriously hard to inspect. We argue that there are at least two reasons making inspectability challenging: (i) representations are distributed across hundreds of channels and (ii) a unifying metric quantifying inspectability is lacking. In this paper, we address both issues by proposing Semantic Bottlenecks (SB), integrated into pretrained networks, to align channel outputs with individual visual concepts and introduce the model agnostic AUiC metric to measure the alignment. We present a case study on semantic segmentation to demonstrate that SBs improve the AUiC up to four-fold over regular network outputs. We explore two types of SB-layers in this work: while concept-supervised SB-layers (SSB) offer the greatest inspectability, we show that the second type, unsupervised SBs (USB), can match the SSBs by producing one-hot encodings. Importantly, for both SB types, we can recover state of the art segmentation performance despite a drastic dimensionality reduction from 1000s of non aligned channels to 10s of semantics-aligned channels that all downstream results are based on
Chordless Cycle Packing Is Fixed-Parameter Tractable
Get PDFA chordless cycle or hole in a graph G is an induced cycle of length at least 4. In the Hole Packing problem, a graph G and an integer k is given, and the task is to find (if exists) a set of k pairwise vertex-disjoint chordless cycles. Our main result is showing that Hole Packing is fixed-parameter tractable (FPT), that is, can be solved in time f(k)n^O(1) for some function f depending only on k
Exorcising Spectres with Secure Compilers
Get PDFAttackers can access sensitive information of programs by exploiting the side-effects of speculatively-executed instructions using Spectre attacks. To mitigate theses attacks, popular compilers deployed a wide range of countermeasures. The security of these countermeasures, however, has not been ascertained: while some of them are believed to be secure, others are known to be insecure and result in vulnerable programs. To reason about the security guarantees of these compiler-inserted countermeasures, this paper presents a framework comprising several secure compilation criteria characterizing when compilers produce code resistant against Spectre attacks. With this framework, we perform a comprehensive security analysis of compiler-level countermeasures against Spectre attacks implemented in major compilers. This work provides sound foundations to formally reason about the security of compiler-level countermeasures against Spectre attacks as well as the first proofs of security and insecurity of said countermeasures
Explainable Data Decompositions
Get PDFOur goal is to discover the components of a dataset, characterize why we deem these components, explain how these components are different from each other, as well as identify what properties they share among each other. As is usual, we consider regions in the data to be components if they show significantly different distributions. What is not usual, however, is that we parameterize these distributions with patterns that are informative for one or more components. We do so because these patterns allow us to characterize what is going on in our data as well as explain our decomposition.
We define the problem in terms of a regularized maximum likelihood, in which we use the Maximum Entropy principle to model each data component with a set of patterns. As the search space is large and unstructured, we propose the deterministic Disc algorithm to efficiently discover high-quality decompositions via an alternating optimization approach. Empirical evaluation on synthetic and real-world data shows that efficiently discovers meaningful components and accurately characterises these in easily understandable terms
Membership Inference Against DNA Methylation Databases
Get PDFBiomedical data sharing is one of the key elements
fostering the advancement of biomedical research but poses
severe risks towards the privacy of individuals contributing
their data, as already demonstrated for genomic data. In
this paper, we study whether and to which extent DNA
methylation data, one of the most important epigenetic
elements regulating human health, is prone to membership
inference attacks, a critical type of attack that reveals an
individual’s participation in a given database. We design
and evaluate three different attacks exploiting published
summary statistics, among which one is based on machine
learning and another is exploiting the dependencies between
genome and methylation data. Our extensive evaluation on
six datasets containing a diverse set of tissues and diseases
collected from more than 1,300 individuals in total shows that
such membership inference attacks are effective, even when
the target’s methylation profile is not accessible. It further
shows that the machine-learning approach outperforms the
statistical attacks, and that learned models are transferable
across different datasets
VisualPhishNet: Zero-Day Phishing Website Detection by Visual Similarity
Get PDFPhishing websites are still a major threat in today's Internet ecosystem.
Despite numerous previous efforts, similarity-based detection methods do not offer sufficient protection for the trusted websites -- in particular against
unseen phishing pages.
This paper contributes VisualPhishNet, a new similarity-based phishing detection framework, based on a triplet Convolutional Neural Network (CNN).
VisualPhishNet learns profiles for websites in order to detect phishing websites by a similarity metric that can generalize to pages with new visual appearances.
We furthermore present VisualPhish, the largest dataset to date that facilitates visual phishing detection in an ecologically valid manner.
We show that our method outperforms previous visual similarity phishing detection approaches by a large margin while being robust against a range of evasion attacks
Synthetic Convolutional Features for Improved Semantic Segmentation
No full textRecently, learning-based image synthesis has enabled to generate high-resolution images, either applying popular adversarial training or a powerful perceptual loss. However, it remains challenging to successfully leverage synthetic data for improving semantic segmentation with additional synthetic images. Therefore, we suggest to generate intermediate convolutional features and propose the first synthesis approach that is catered to such intermediate convolutional features. This allows us to generate new features from label masks and include them successfully into the training procedure in order to improve the performance of semantic segmentation. Experimental results and analysis on two challenging datasets Cityscapes and ADE20K show that our generated feature improves performance on segmentation tasks