Helmholtz Center for Information Security
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Rate-1 Trapdoor Functions from the Diffie-Hellman Problem
Get PDFTrapdoor functions (TDFs) are one of the fundamental building blocks in cryptography. Studying the underlying assumptions and the efficiency of the resulting instantiations is therefore of both theoretical and practical interest. In this work we improve the input-to-image rate of TDFs based on the Diffie-Hellman problem. Specifically, we present: (a)A rate-1 TDF from the computational Diffie-Hellman (CDH) assumption, improving the result of Garg, Gay, and Hajiabadi [EUROCRYPT 2019], which achieved linear-size outputs but with large constants. Our techniques combine non-binary alphabets and high-rate error-correcting codes over large fields.(b)A rate-1 deterministic public-key encryption satisfying block-source security from the decisional Diffie-Hellman (DDH) assumption. While this question was recently settled by Döttling et al. [CRYPTO 2019], our scheme is conceptually simpler and concretely more efficient. We demonstrate this fact by implementing our construction
AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning
Get PDFPerceptual ad-blocking is a novel approach that detects online advertisements based on their visual content. Compared to traditional filter lists, the use of perceptual signals is believed to be less prone to an arms race with web publishers and ad networks. We demonstrate that this may not be the case. We describe attacks on multiple perceptual ad-blocking techniques, and unveil a new arms race that likely disfavors ad-blockers. Unexpectedly, perceptual ad-blocking can also introduce new vulnerabilities that let an attacker bypass web security boundaries and mount DDoS attacks.
We first analyze the design space of perceptual ad-blockers and present a unified architecture that incorporates prior academic and commercial work. We then explore a variety of attacks on the ad-blocker's detection pipeline, that enable publishers or ad networks to evade or detect ad-blocking, and at times even abuse its high privilege level to bypass web security boundaries. On the one hand, we show that perceptual ad-blocking must visually classify rendered web content to escape an arms race centered on obfuscation of page markup. On the other, we present a concrete set of attacks on visual ad-blockers by constructing adversarial examples in a real web page context. For seven ad-detectors, we create perturbed ads, ad-disclosure logos, and native web content that misleads perceptual ad-blocking with 100% success rates. In one of our attacks, we demonstrate how a malicious user can upload adversarial content, such as a perturbed image in a Facebook post, that fools the ad-blocker into removing another users' non-ad content.
Moving beyond the Web and visual domain, we also build adversarial examples for AdblockRadio, an open source radio client that uses machine learning to detects ads in raw audio streams
Evaluating Fault Localization for Resource Adaptation via Test-Based Software Modification
No full textThe ability to dynamically adapt to resource variations is critical for modern-day mission-critical systems that operate in ever-changing resource environments. Test-based Software Modification (TBSM) is a recently proposed technique to build Resource Adaptive Software (RAS) that relies on existing test infrastructure, test labeling, and program modifications. TBSM is simple and applicable, but an inefficient technique; the primary reason for inefficiency is the sheer size of the search space. In this paper, we propose AdFL, a repurposing of Fault Localization (FL) that can shrink (and prioritize) the search space for TBSM more effectively than previously proposed heuristics. We present complete case studies and an empirical analysis of a set of open source projects as evidence that AdFL can significantly reduce the search space in TBSM. We show how to combine AdFL with previous heuristics for TBSM, and propose an incremental, best-effort variant of TBSM that uses AdFL to prioritize the search
GDPR-Compliant Reputation System Based on Self-certifying Domain Signatures
No full textCreating a distributed reputation system compliant with the GDPR Regulation faces a number of problems. Each record should be protected regarding its integrity and origin, while the record’s author should remain anonymous, as long as there is no justified legal reason to reveal his real identity. Thereby, the standard digital signatures cannot be applied to secure the records.
In this paper we propose a Privacy Aware Distributed Reputation Evaluation system, where each subject of evaluation holds its recommendation record. By application of a novel technique of domain signatures we are able to guarantee that (a) integrity of each entry is strongly protected; in particular, the evaluation subject cannot modify it, (b) the author of each entry is anonymous, however all entries of the same author on the same subject appear under the same pseudonym (so the Sybil attacks are repelled), (c) the entries corresponding to the same author but for different evaluation subjects are unlinkable, (d) only registered users can create valid entries, (e) the real identity of the author of an entry can be revealed by relevant authorities by running a multi-party protocol, (f) for each entry one can create a pseudorandom key in a deterministic way.
The first five features correspond directly to the requirements of the GDPR Regulation. In particular, they guard against profiling the users based on the entries created by them.
In order to facilitate practical applications we propose to maintain a pseudorandom sample of all entries concerning a given evaluation subject. We show how to guarantee that the sample is fairly chosen despite the fact that the sample is kept by the evaluation subject. We present a few strategies enabling to mimic some important probability distributions for choosing the sample
Real-time Stream Monitoring with StreamLAB
Get PDFThe online evaluation of real-time data streams is an essential part of cyber-physical systems and plays a crucial role when monitoring those that are intrinsically safety-critical. We present StreamLAB, a specification and monitoring framework for stream-based real-time properties, which has already been applied on autonomous aerial vehicles. As a stream-based monitoring engine, StreamLAB translates input streams, for example sensor readings or other data collected at runtime, into output streams containing aggregated statistics. In practice, input data often arrives in varying, unpredictable frequencies and a monitor has to cope with limited memory and computational resources. StreamLAB tackles these problems by combining the benefits of a programming language and a formal, temporal logic. While it allows for expressing a vast variety of real-time properties, specifications are also compositional and easy to reuse by design, and StreamLAB can compute guaranteed upper bounds on the memory requirements. A monitor for a given specification will respect these bounds and thus never disrupt the normal operation of a system, notwithstanding the frequency of arriving input values. This makes StreamLAB a suitable choice for embedded devices with strictly limited memory
MBeacon: Privacy-Preserving Beacons for DNA Methylation Data
Get PDFThe advancement of molecular profiling techniques
fuels biomedical research with a deluge of data. To facilitate
data sharing, the Global Alliance for Genomics and Health
established the Beacon system, a search engine designed to help
researchers find datasets of interest. While the current Beacon
system only supports genomic data, other types of biomedical
data, such as DNA methylation, are also essential for advancing
our understanding in the field. In this paper, we propose the first
Beacon system for DNA methylation data sharing: MBeacon. As
the current genomic Beacon is vulnerable to privacy attacks, such
as membership inference, and DNA methylation data is highly
sensitive, we take a privacy-by-design approach to construct
MBeacon.
First, we demonstrate the privacy threat, by proposing a
membership inference attack tailored specifically to unprotected
methylation Beacons. Our experimental results show that 100
queries are sufficient to achieve a successful attack with AUC
(area under the ROC curve) above 0.9. To remedy this situation,
we propose a novel differential privacy mechanism, namely SVT2
,
which is the core component of MBeacon. Extensive experiments
over multiple datasets show that SVT2
can successfully mitigate
membership privacy risks without significantly harming utility.
We further implement a fully functional prototype of MBeacon
which we make available to the research communit
Geo-locating Drivers: A Study of Sensitive Data Leakage in Ride-Hailing Services.
Get PDFIncreasingly, mobile application-based ride-hailing
services have become a very popular means of transportation.
Due to the handling of business logic, these services also contain
a wealth of privacy-sensitive information such as GPS locations,
car plates, driver licenses, and payment data. Unlike many of
the mobile applications in which there is only one type of users,
ride-hailing services face two types of users: riders and drivers.
While most of the efforts had focused on the rider’s privacy,
unfortunately, we notice little has been done to protect drivers.
To raise the awareness of the privacy issues with drivers, in
this paper we perform the first systematic study of the drivers’
sensitive data leakage in ride-hailing services. More specifically,
we select 20 popular ride-hailing apps including Uber and Lyft
and focus on one particular feature, namely the nearby cars
feature. Surprisingly, our experimental results show that largescale
data harvesting of drivers is possible for all of the ridehailing
services we studied. In particular, attackers can determine
with high-precision the driver’s privacy-sensitive information
including mostly visited address (e.g., home) and daily driving behaviors.
Meanwhile, attackers can also infer sensitive information
about the business operations and performances of ride-hailing
services such as the number of rides, utilization of cars, and
presence on the territory. In addition to presenting the attacks,
we also shed light on the countermeasures the service providers
could take to protect the driver’s sensitive information
"If HTTPS Were Secure, I Wouldn't Need 2FA" - End User and Administrator Mental Models of HTTPS
Get PDFHTTPS is one of the most important protocols used
to secure communication and is, fortunately, becoming more
pervasive. However, especially the long tail of websites is still not
sufficiently secured. HTTPS involves different types of users, e.g.,
end users who are forced to make security decisions when faced
with warnings or administrators who are required to deal with
cryptographic fundamentals and complex decisions concerning
compatibility.
In this work, we present the first qualitative study of both
end user and administrator mental models of HTTPS. We interviewed 18 end users and 12 administrators; our findings reveal
misconceptions about security benefits and threat models from
both groups. We identify protocol components that interfere with
secure configurations and usage behavior and reveal differences
between administrator and end user mental models.
Our results suggest that end user mental models are more
conceptual while administrator models are more protocol-based.
We also found that end users often confuse encryption with
authentication, significantly underestimate the security benefits
of HTTPS. They also ignore and distrust security indicators
while administrators often do not understand the interplay of
functional protocol components. Based on the different mental
models, we discuss implications and provide actionable recommendations for future designs of user interfaces and protocols
Ring Signatures: Logarithmic Size, No Setup -- from Standard Assumptions
Get PDFRing signatures allow for creating signatures on behalf of an ad hoc group of signers,
hiding the true identity of the signer among the group. A natural goal is to
construct a ring signature scheme for which the signature size is short in the
number of ring members. Moreover, such a construction should not rely on a trusted setup and be proven secure under falsifiable standard assumptions. Despite many years of research this question is still open.
In this paper, we present the first construction of logarithmic-size ring signatures which do not rely on a trusted setup or the random oracle heuristic. Specifically, our scheme can be instantiated from standard assumptions and
the size of signatures grows only logarithmically in the number of ring members.
We also extend our techniques to the setting of linkable ring signatures, where signatures created using the same signing key can be linked
Why does this App need this Data? Automatic Tightening of Resource Access
No full textOn mobile operating systems, apps may access resources that are not be needed for their primary functionality.
Which are the resources an app actually needs for its core functionality? And what happens if we deny access to other resources?
Using a test generator for user interaction, we systematically explore app behavior under varied resource constraints and determine the impact of access restrictions, yielding a minimal set of required privileges for each app and functionality.
In our proof of concept on Android apps, our TIARA prototype could block up to 69% of resource accesses while retaining all previously explored functionality