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p628-cangialosi.pdf,|
The semantics of online authentication in the web are rather
straightforward:
if Alice has a certificate binding Bobs
name to a public key, and if a remote entity can prove knowl-
edge of Bobs private key, then (barring key compromise)
that remote entity must be Bob. However, in reality, many
websitesand the majority of the most popular onesare
hosted at least in part by third parties such as Content Deliv-
ery Networks (CDNs) or web hosting providers. Put simply:
administrators of websites who deal with (extremely) sensi-
tive user data are giving their private keys to third parties.
Importantly, this sharing of keys is undetectable by most
users, and widely unknown even among researchers.
In this paper, we perform a large-scale measurement study
of key sharing in todays web. We analyze the prevalence
with which websites trust third-party hosting providers with
their secret keys, as well as the impact that this trust has on
responsible key management practices, such as revocation.
Our results reveal that key sharing is extremely common,
with a small handful of hosting providers having keys from
the majority of the most popular websites. We also find that
hosting providers often manage their customers keys, and
that they tend to react more slowly yet more thoroughly to
compromised or potentially compromised keys.
1.
|,Data
p1639-pereida-garcia.pdf,|
TLS and SSH are two of the most commonly used proto-
cols for securing Internet traffic. Many of the implemen-
tations of these protocols rely on the cryptographic primi-
tives provided in the OpenSSL library. In this work we dis-
close a vulnerability in OpenSSL, affecting all versions and
forks (e.g. LibreSSL and BoringSSL) since roughly October
2005, which renders the implementation of the DSA signa-
ture scheme vulnerable to cache-based side-channel attacks.
Exploiting the software defect, we demonstrate the first pub-
lished cache-based key-recovery attack on these protocols:
260 SSH-2 handshakes to extract a 1024/160-bit DSA host
key from an OpenSSH server, and 580 TLS 1.2 handshakes
to extract a 2048/256-bit DSA key from an stunnel server.
Keywords
applied cryptography; digital signatures; side-channel anal-
ysis; timing attacks; cache-timing attacks; DSA; OpenSSL;
CVE-2016-2178
1.
|,Data
p981-lewi.pdf,|
Secure multilinear maps (mmaps) have been shown to have
remarkable applications in cryptography, such as multi-input
functional encryption (MIFE) and program obfuscation. To
date, there has been little evaluation of the performance of
these applications.
In this paper we initiate a systematic
study of mmap-based constructions. We build a general
framework, called 5Gen, to experiment with these applica-
tions. At the top layer we develop a compiler that takes
in a high-level program and produces an optimized matrix
branching program needed for the applications we consider.
Next, we optimize and experiment with several MIFE and
obfuscation constructions and evaluate their performance.
The 5Gen framework is modular and can easily accommo-
date new mmap constructions as well as new MIFE and
obfuscation constructions, as well as being an open-source
tool that can be used by other research groups to experiment
with a variety of mmap-based constructions.
1.
|,Non-data
sec14-paper-tripp.pdf,|
Mobile apps often require access to private data, such
as the device ID or location. At the same time, popular
platforms like Android and iOS have limited support for
user privacy. This frequently leads to unauthorized dis-
closure of private information by mobile apps, e.g. for
advertising and analytics purposes. This paper addresses
the problem of privacy enforcement in mobile systems,
which we formulate as a classification problem: When
arriving at a privacy sink (e.g., database update or outgo-
ing web message), the runtime system must classify the
sinks behavior as either legitimate or illegitimate. The
traditional approach of information-flow (or taint) track-
ing applies binary classification, whereby information
release is legitimate iff there is no data flow from a pri-
vacy source to sink arguments. While this is a useful
heuristic, it also leads to false alarms.
We propose to address privacy enforcement as a learn-
ing problem, relaxing binary judgments into a quanti-
tative/probabilistic mode of reasoning. Specifically, we
propose a Bayesian notion of statistical classification,
which conditions the judgment whether a release point is
legitimate on the evidence arising at that point. In our
concrete approach, implemented as the BAYESDROID
system that is soon to be featured in a commercial prod-
uct, the evidence refers to the similarity between the data
values about to be released and the private data stored on
the device. Compared to TaintDroid, a state-of-the-art
taint-based tool for privacy enforcement, BAYESDROID
is substantially more accurate. Applied to 54 top-popular
Google Play apps, BAYESDROID is able to detect 27 pri-
vacy violations with only 1 false alarm.
1
|,Data
07259368.pdf,|-This
extraction
dimension
series.
is considered
attacks.
transforming
variance dimension
stationarity
are extracted
extracted
set of features
trajectory
attack when the denoised
This technique
variance
in
finding changing patterns
of a data series due to the
presence
is not dependent
measurement
technique
windows in a highly non stationary
to remove high variability
noise. The
of an
the presence
dimensions
in data series.
of noise and denial of service
attack because it
and mono-scale
and locally stationary
fractal dimension.
shows increasing
of the trajectory
of variations
Then features
data series.
dimension
estimates
There are various
where dimensions
the complexity
of an
to
Multifractal
object in a non-integer
monoscale analysis
integers
only. The concept of non-integer
arises due to the power law relationship
samples.
from a practical
Method [3], the Katz-Servcik
Fluctuation
Analysis
method that
fractal
measures
scale for a
given data series [7]. In this work, we utilized
fractal
to characterize
data series for the identification
attack.
Analysis
is another
at multiple
(VFD) estimate
the change in variance
Analysis [5], Power Spectral
of the presence
of an
and Critical
dimension
estimates
dimension
Estimation
aspect,
keeping statistical
of stationarity
of a data series
is superior
of fractal
Moreover,
this
indicates
Exponent
on integer
in the data
adaptive
provides
a DNS count
is another
of fractal features
Density
[6]. Variance
that the rate of change of variance
method [4], Detrended
dimension
dimension
variance
time series such as the Box-Counting
Keywords-Cognitive Machine Learning, Chaos, Fractal,
DNS, DDoS Amplification,
Internet, Anomaly Detection,
Cyber threats, Data traffic, Multifractal,
Dimension, Wavelet, Haar function,
Sationary,
Series Analysis.
Change Detection,
Non
Window, Time
Trend Analysis, Adaptive Sliding
Variance Fractal
I. |,Data
p1204-fett.pdf,|
The OAuth 2.0 protocol is one of the most widely deployed au-
thorization/single sign-on (SSO) protocols and also serves as the
foundation for the new SSO standard OpenID Connect. Despite the
popularity of OAuth, so far analysis efforts were mostly targeted at
finding bugs in specific implementations and were based on formal
models which abstract from many web features or did not provide
a formal treatment at all.
In this paper, we carry out the first extensive formal analysis of
the OAuth 2.0 standard in an expressive web model. Our analy-
sis aims at establishing strong authorization, authentication, and
session integrity guarantees, for which we provide formal defini-
tions. In our formal analysis, all four OAuth grant types (autho-
rization code grant, implicit grant, resource owner password cre-
dentials grant, and the client credentials grant) are covered. They
may even run simultaneously in the same and different relying par-
ties and identity providers, where malicious relying parties, identity
providers, and browsers are considered as well. Our modeling and
analysis of the OAuth 2.0 standard assumes that security recommen-
dations and best practices are followed in order to avoid obvious
and known attacks.
When proving the security of OAuth in our model, we discovered
four attacks which break the security of OAuth. The vulnerabilities
can be exploited in practice and are present also in OpenID Connect.
We propose fixes for the identified vulnerabilities, and then, for
the first time, actually prove the security of OAuth in an expressive
web model. In particular, we show that the fixed version of OAuth
(with security recommendations and best practices in place) pro-
vides the authorization, authentication, and session integrity proper-
ties we specify.
1.
|,Non-data
imc182-meiklejohnA.pdf,|
Bitcoin is a purely online virtual currency, unbacked by either phys-
ical commodities or sovereign obligation; instead, it relies on a
combination of cryptographic protection and a peer-to-peer proto-
col for witnessing settlements. Consequently, Bitcoin has the un-
intuitive property that while the ownership of money is implicitly
anonymous, its flow is globally visible. In this paper we explore
this unique characteristic further, using heuristic clustering to group
Bitcoin wallets based on evidence of shared authority, and then us-
ing re-identification attacks (i.e., empirical purchasing of goods and
services) to classify the operators of those clusters. From this anal-
ysis, we characterize longitudinal changes in the Bitcoin market,
the stresses these changes are placing on the system, and the chal-
lenges for those seeking to use Bitcoin for criminal or fraudulent
purposes at scale.
Categories and Subject Descriptors
K.4.4 [Electronic Commerce]: Payment schemes
Keywords
Bitcoin; Measurement; Anonymity
1.
|,Data
06547112.pdf,|We consider interactive, proof-based verifiable computa-
tion: how can a client machine specify a computation to a server,
receive an answer, and then engage the server in an interactive
protocol that convinces the client that the answer is correct, with
less work for the client than executing the computation in the first
place? Complexity theory and cryptography offer solutions in prin-
ciple, but if implemented naively, they are ludicrously expensive.
Recently, however, several strands of work have refined this the-
ory and implemented the resulting protocols in actual systems. This
work is promising but suffers from one of two problems: either it
relies on expensive cryptography, or else it applies to a restricted
class of computations. Worse, it is not always clear which protocol
will perform better for a given problem.
We describe a system that (a) extends optimized refinements of
the non-cryptographic protocols to a much broader class of compu-
tations, (b) uses static analysis to fail over to the cryptographic ones
when the non-cryptographic ones would be more expensive, and (c)
incorporates this core into a built system that includes a compiler
for a high-level language, a distributed server, and GPU accelera-
tion. Experimental results indicate that our system performs better
and applies more widely than the best in the literature.
1 |,Non-data
sec14-paper-costin.pdf,|
1 |,Data
sec14-paper-li-zhigong.pdf,|
Users speaking different languages may prefer different
patterns in creating their passwords, and thus knowledge
on English passwords cannot help to guess passwords
from other languages well. Research has already shown
Chinese passwords are one of the most difficult ones to
guess. We believe that the conclusion is biased because,
to the best of our knowledge, little empirical study has
examined regional differences of passwords on a large
scale, especially on Chinese passwords. In this paper, we
study the differences between passwords from Chinese
and English speaking users, leveraging over 100 million
leaked and publicly available passwords from Chinese
and international websites in recent years. We found that
Chinese prefer digits when composing their passwords
while English users prefer letters, especially lowercase
letters. However, their strength against password guess-
ing is similar. Second, we observe that both users pre-
fer to use the patterns that they are familiar with, e.g.,
Chinese Pinyins for Chinese and English words for En-
glish users. Third, we observe that both Chinese and En-
glish users prefer their conventional format when they
use dates to construct passwords. Based on these obser-
vations, we improve a PCFG (Probabilistic Context-Free
Grammar) based password guessing method by inserting
Pinyins (about 2.3% more entries) into the attack dictio-
nary and insert our observed composition rules into the
guessing rule set. As a result, our experiments show that
the efficiency of password guessing increases by 34%.
1
|,Data
sec14-paper-blond.pdf,|
We present an empirical analysis of targeted attacks
against a human-rights Non-Governmental Organization
(NGO) representing a minority living in China. In par-
ticular, we analyze the social engineering techniques, at-
tack vectors, and malware employed in malicious emails
received by two members of the NGO over a four-year
period. We find that both the language and topic of
the emails were highly tailored to the victims, and that
sender impersonation was commonly used to lure them
into opening malicious attachments. We also show that
the majority of attacks employed malicious documents
with recent but disclosed vulnerabilities that tend to
evade common defenses. Finally, we find that the NGO
received malware from different families and that over a
quarter of the malware can be linked to entities that have
been reported to engage in targeted attacks against polit-
ical and industrial organizations, and Tibetan NGOs.
1 |,Data
1512.03916.pdf,|In the last decade many works has been done on the
Internet topology at router or autonomous system (AS) level. As
routers is the essential composition of ASes while ASes dominate
the behavior of their routers. It is no doubt that identifying the
affiliation between routers and ASes can let us gain a deeper
understanding on the topology. However, the existing methods
that assign a router to an AS just based on the origin ASes of its IP
addresses, which does not make full use of information in our hand.
In this paper, we propose a methodology to assign routers to their
owner ASes based on community discovery tech. First, we use the
origin ASes information along with router-pairs similarities to
construct a weighted router level topology; secondly, for enormous
topology data (more than 2M nodes and 19M edges) from CAIDA
ITDK project, we propose a fast hierarchy clustering which time
and space complex are both linear to do ASes community
discovery, last we do router-to-AS mapping based on these ASes
communities. Experiments show that combining with ASes
communities our methodology discovers, the best accuracy rate of
router-to-AS mapping can reach to 82.62%, which is drastically
high comparing to prior works that stagnate on 65.44%.
Keywordsrouter-to-AS mapping; community discovery;
global router topology; fast hierarchy clustering
I. |,Data
p1591-green.pdf,|
We present a protocol to enable privacy preserving adver-
tising reporting at scale. Unlike previous systems, our work
scales to millions of users and tens of thousands of distinct
ads. Our approach builds on the homomorphic encryption
approach proposed by Adnostic [42], but uses new crypto-
graphic proof techniques to efficiently report billions of ad
impressions a day using an additively homomorphic voting
schemes. Most importantly, our protocol scales without im-
posing high loads on trusted third parties. Finally, we inves-
tigate a cost effective method to privately deliver ads with
computational private information retrieval.
1.
|,Non-data
06547120.pdf,|Perceptual, context-aware applications that ob-
serve their environment and interact with users via cameras
and other sensors are becoming ubiquitous on personal com-
puters, mobile phones, gaming platforms, household robots,
and augmented-reality devices. This raises new privacy risks.
We describe the design and implementation of DARKLY, a
practical privacy protection system for the increasingly com-
mon scenario where an untrusted, third-party perceptual ap-
plication is running on a trusted device. DARKLY is integrated
with OpenCV, a popular computer vision library used by such
applications to access visual inputs. It deploys multiple privacy
protection mechanisms, including access control, algorithmic
privacy transforms, and user audit.
We evaluate DARKLY on 20 perceptual applications that per-
form diverse tasks such as image recognition, object tracking,
security surveillance, and face detection. These applications
run on DARKLY unmodified or with very few modifications
and minimal performance overheads vs. native OpenCV. In
most cases, privacy enforcement does not reduce the appli-
cations functionality or accuracy. For the rest, we quantify
the tradeoff between privacy and utility and demonstrate that
utility remains acceptable even with strong privacy protection.
I. |,Data
p17-luu.pdf,|
Cryptocurrencies, such as Bitcoin and 250 similar alt-coins, em-
body at their core a blockchain protocol a mechanism for a dis-
tributed network of computational nodes to periodically agree on
a set of new transactions. Designing a secure blockchain protocol
relies on an open challenge in security, that of designing a highly-
scalable agreement protocol open to manipulation by byzantine or
arbitrarily malicious nodes. Bitcoins blockchain agreement proto-
col exhibits security, but does not scale: it processes 37 transac-
tions per second at present, irrespective of the available computa-
tion capacity at hand.
In this paper, we propose a new distributed agreement proto-
col for permission-less blockchains called ELASTICO. ELASTICO
scales transaction rates almost linearly with available computation
for mining: the more the computation power in the network, the
higher the number of transaction blocks selected per unit time.
ELASTICO is efficient in its network messages and tolerates byzan-
tine adversaries of up to one-fourth of the total computational power.
Technically, ELASTICO uniformly partitions or parallelizes the min-
ing network (securely) into smaller committees, each of which pro-
cesses a disjoint set of transactions (or shards). While sharding
is common in non-byzantine settings, ELASTICO is the first candi-
date for a secure sharding protocol with presence of byzantine ad-
versaries. Our scalability experiments on Amazon EC2 with up to
1, 600 nodes confirm ELASTICOs theoretical scaling properties.
1.
|,Non-data
p871-zhou.pdf,|
We present a software approach to mitigate access-driven
side-channel attacks that leverage last-level caches (LLCs)
shared across cores to leak information between security do-
mains (e.g., tenants in a cloud). Our approach dynami-
cally manages physical memory pages shared between secu-
rity domains to disable sharing of LLC lines, thus prevent-
ing Flush-Reload side channels via LLCs. It also man-
ages cacheability of memory pages to thwart cross-tenant
Prime-Probe attacks in LLCs. We have implemented
our approach as a memory management subsystem called
CacheBar within the Linux kernel to intervene on such
side channels across container boundaries, as containers are
a common method for enforcing tenant isolation in Platform-
as-a-Service (PaaS) clouds. Through formal verification,
principled analysis, and empirical evaluation, we show that
CacheBar achieves strong security with small performance
overheads for PaaS workloads.
Keywords
Cache-based side channel; prime-probe; flush-reload
1.
|,Non-data
p1480-albrecht.pdf,|
This work presents a systematic analysis of symmetric encryp-
tion modes for SSH that are in use on the Internet, providing
deployment statistics, new attacks, and security proofs for
widely used modes. We report deployment statistics based on
two Internet-wide scans of SSH servers conducted in late 2015
and early 2016. Dropbear and OpenSSH implementations
dominate in our scans. From our first scan, we found 130,980
OpenSSH servers that are still vulnerable to the CBC-mode-
specific attack of Albrecht et al. (IEEE S&P 2009), while we
found a further 20,000 OpenSSH servers that are vulnerable
to a new attack on CBC-mode that bypasses the counter-
measures introduced in OpenSSH 5.2 to defeat the attack of
Albrecht et al. At the same time, 886,449 Dropbear servers in
our first scan are vulnerable to a variant of the original CBC-
mode attack. On the positive side, we provide formal security
analyses for other popular SSH encryption modes, namely
ChaCha20-Poly1305, generic Encrypt-then-MAC, and AES-
GCM. Our proofs hold for detailed pseudo-code descriptions
of these algorithms as implemented in OpenSSH. Our proofs
use a corrected and extended version of the fragmented de-
cryption security model that was specifically developed for
the SSH setting by Boldyreva et al. (Eurocrypt 2012). These
proofs provide strong confidentiality and integrity guaran-
tees for these alternatives to CBC-mode encryption in SSH.
However, we also show that these alternatives do not meet ad-
ditional, desirable notions of security (boundary-hiding under
passive and active attacks, and denial-of-service resistance)
that were formalised by Boldyreva et al.
1.
|,Data
p468-checkoway.pdf,|
In December 2015, Juniper Networks announced multiple security
vulnerabilities stemming from unauthorized code in ScreenOS, the
operating system for their NetScreen VPN routers. The more so-
phisticated of these vulnerabilities was a passive VPN decryption
capability, enabled by a change to one of the elliptic curve points
used by the Dual EC pseudorandom number generator.
In this paper, we describe the results of a full independent analysis
of the ScreenOS randomness and VPN key establishment proto-
col subsystems, which we carried out in response to this incident.
While Dual EC is known to be insecure against an attacker who
can choose the elliptic curve parameters, Juniper had claimed in
2013 that ScreenOS included countermeasures against this type of
attack. We find that, contrary to Junipers public statements, the
ScreenOS VPN implementation has been vulnerable since 2008 to
passive exploitation by an attacker who selects the Dual EC curve
point. This vulnerability arises due to apparent flaws in Junipers
countermeasures as well as a cluster of changes that were all in-
troduced concurrently with the inclusion of Dual EC in a single
2008 release. We demonstrate the vulnerability on a real NetScreen
device by modifying the firmware to install our own parameters,
and we show that it is possible to passively decrypt an individual
VPN session in isolation without observing any other network traffic.
We investigate the possibility of passively fingerprinting ScreenOS
implementations in the wild. This incident is an important example
of how guidelines for random number generation, engineering, and
validation can fail in practice.
1.
|,Data
p1438-krawczyk.pdf,|
We study the question of how to build compilers that
transform a unilaterally authenticated (UA) key-exchange
protocol into a mutually-authenticated (MA) one. We present
a simple and efficient compiler and characterize the UA pro-
tocols that the compiler upgrades to the MA model, showing
this to include a large and important class of UA protocols.
The question, while natural, has not been studied widely.
Our work is motivated in part by the ongoing work on the
design of TLS 1.3, specifically the design of the client au-
thentication mechanisms including the challenging case of
post-handshake authentication. Our approach supports the
analysis of these mechanisms in a general and modular way,
in particular aided by the notion of functional security that
we introduce as a generalization of key exchange models and
which may be of independent interest.
1.
|,Non-data
p1528-sharif.pdf,|
Machine learning is enabling a myriad innovations, including
new algorithms for cancer diagnosis and self-driving cars.
The broad use of machine learning makes it important to
understand the extent to which machine-learning algorithms
are subject to attack, particularly when used in applications
where physical security or safety is at risk.
In this paper, we focus on facial biometric systems, which
are widely used in surveillance and access control. We de-
fine and investigate a novel class of attacks: attacks that
are physically realizable and inconspicuous, and allow an at-
tacker to evade recognition or impersonate another individ-
ual. We develop a systematic method to automatically gen-
erate such attacks, which are realized through printing a pair
of eyeglass frames. When worn by the attacker whose image
is supplied to a state-of-the-art face-recognition algorithm,
the eyeglasses allow her to evade being recognized or to im-
personate another individual. Our investigation focuses on
white-box face-recognition systems, but we also demonstrate
how similar techniques can be used in black-box scenarios,
as well as to avoid face detection.
1.
|,Data
p755-liao.pdf,|
To adapt to the rapidly evolving landscape of cyber threats, secu-
rity professionals are actively exchanging Indicators of Compro-
mise (IOC) (e.g., malware signatures, botnet IPs) through public
sources (e.g. blogs, forums, tweets, etc.). Such information, of-
ten presented in articles, posts, white papers etc., can be converted
into a machine-readable OpenIOC format for automatic analysis
and quick deployment to various security mechanisms like an in-
trusion detection system. With hundreds of thousands of sources
in the wild, the IOC data are produced at a high volume and veloc-
ity today, which becomes increasingly hard to manage by humans.
Efforts to automatically gather such information from unstructured
text, however, is impeded by the limitations of todays Natural Lan-
guage Processing (NLP) techniques, which cannot meet the high
standard (in terms of accuracy and coverage) expected from the
IOCs that could serve as direct input to a defense system.
In this paper, we present iACE, an innovation solution for fully
automated IOC extraction. Our approach is based on the obser-
vation that the IOCs in technical articles are often described in a
predictable way: being connected to a set of context terms (e.g.,
download) through stable grammatical relations. Leveraging this
observation, iACE is designed to automatically locate a putative
IOC token (e.g., a zip file) and its context (e.g., malware, down-
load) within the sentences in a technical article, and further an-
alyze their relations through a novel application of graph mining
techniques. Once the grammatical connection between the tokens
is found to be in line with the way that the IOC is commonly pre-
sented, these tokens are extracted to generate an OpenIOC item
that describes not only the indicator (e.g., a malicious zip file) but
also its context (e.g., download from an external source). Running
on 71,000 articles collected from 45 leading technical blogs, this
new approach demonstrates a remarkable performance: it gener-
ated 900K OpenIOC items with a precision of 95% and a coverage
over 90%, which is way beyond what the state-of-the-art NLP tech-
nique and industry IOC tool can achieve, at a speed of thousands of
articles per hour. Further, by correlating the IOCs mined from the
articles published over a 13-year span, our study sheds new light on
1The two lead authors are ordered alphabetically.
Permission to make digital or hard copies of all or part of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for profit or commercial advantage and that copies bear this notice and the full cita-
tion on the first page. Copyrights for components of this work owned by others than
ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or re-
publish, to post on servers or to redistribute to lists, requires prior specific permission
and/or a fee. Request permissions from [email protected].
CCS16, October 24-28, 2016, Vienna, Austria
c(cid:13) 2016 ACM. ISBN 978-1-4503-4139-4/16/10. . . $15.00
DOI: http://dx.doi.org/10.1145/2976749.2978315
Figure 1: Example of OpenIOC schema.
the links across hundreds of seemingly unrelated attack instances,
particularly their shared infrastructure resources, as well as the im-
pacts of such open-source threat intelligence on security protection
and evolution of attack strategies.
1.
|,Data
sec14-paper-demmler.pdf,|
Secure two-party computation allows two mutually dis-
trusting parties to jointly compute an arbitrary function
on their private inputs without revealing anything but the
result. An interesting target for deploying secure compu-
tation protocols are mobile devices as they contain a lot
of sensitive user data. However, their resource restriction
makes the deployment of secure computation protocols a
challenging task.
In this work, we optimize and implement
the
secure computation protocol by Goldreich-Micali-
Wigderson (GMW) on mobile phones. To increase per-
formance, we extend the protocol by a trusted hardware
token (i.e., a smartcard). The trusted hardware token al-
lows to pre-compute most of the workload in an initial-
ization phase, which is executed locally on one device
and can be pre-computed independently of the later com-
munication partner. We develop and analyze a proof-
of-concept implementation of generic secure two-party
computation on Android smart phones making use of
a microSD smartcard. Our use cases include private
set intersection for finding shared contacts and private
scheduling of a meeting with location preferences. For
private set intersection, our token-aided implementation
on mobile phones is up to two orders of magnitude faster
than previous generic secure two-party computation pro-
tocols on mobile phones and even as fast as previous
work on desktop computers.
1
|,Non-data
p55-barthe.pdf,|
Differential privacy is a promising formal approach to data privacy,
which provides a quantitative bound on the privacy cost of an al-
gorithm that operates on sensitive information. Several tools have
been developed for the formal verification of differentially private
algorithms, including program logics and type systems. However,
these tools do not capture fundamental techniques that have emerged
in recent years, and cannot be used for reasoning about cutting-edge
differentially private algorithms. Existing techniques fail to handle
three broad classes of algorithms: 1) algorithms where privacy de-
pends on accuracy guarantees, 2) algorithms that are analyzed with
the advanced composition theorem, which shows slower growth in
the privacy cost, 3) algorithms that interactively accept adaptive
inputs.
We address these limitations with a new formalism extending
apRHL [6], a relational program logic that has been used for proving
differential privacy of non-interactive algorithms, and incorporating
aHL [11], a (non-relational) program logic for accuracy properties.
We illustrate our approach through a single running example, which
exemplifies the three classes of algorithms and explores new variants
of the Sparse Vector technique, a well-studied algorithm from the
privacy literature. We implement our logic in EasyCrypt, and for-
mally verify privacy. We also introduce a novel coupling technique
called optimal subset coupling that may be of independent interest.
1.
|,Non-data
sec15-paper-vanhoef.pdf,|
We present new biases in RC4, break the Wi-Fi Protected
Access Temporal Key Integrity Protocol (WPA-TKIP),
and design a practical plaintext recovery attack against
the Transport Layer Security (TLS) protocol. To empir-
ically find new biases in the RC4 keystream we use sta-
tistical hypothesis tests. This reveals many new biases in
the initial keystream bytes, as well as several new long-
term biases. Our fixed-plaintext recovery algorithms are
capable of using multiple types of biases, and return a
list of plaintext candidates in decreasing likelihood.
To break WPA-TKIP we introduce a method to gen-
erate a large number of identical packets. This packet is
decrypted by generating its plaintext candidate list, and
using redundant packet structure to prune bad candidates.
From the decrypted packet we derive the TKIP MIC key,
which can be used to inject and decrypt packets. In prac-
tice the attack can be executed within an hour. We also
attack TLS as used by HTTPS, where we show how to
decrypt a secure cookie with a success rate of 94% using
9 227 ciphertexts. This is done by injecting known data
around the cookie, abusing this using Mantins ABSAB
bias, and brute-forcing the cookie by traversing the plain-
text candidates. Using our traffic generation technique,
we are able to execute the attack in merely 75 hours.
1
|,Non-data
p1414-liu.pdf,|
In a dangling DNS record (Dare), the resources pointed to by the
DNS record are invalid, but the record itself has not yet been purged
from DNS. In this paper, we shed light on a largely overlooked
threat in DNS posed by dangling DNS records. Our work reveals
that Dare can be easily manipulated by adversaries for domain hi-
jacking. In particular, we identify three attack vectors that an ad-
versary can harness to exploit Dares. In a large-scale measurement
study, we uncover 467 exploitable Dares in 277 Alexa top 10,000
domains and 52 edu zones, showing that Dare is a real, preva-
lent threat. By exploiting these Dares, an adversary can take full
control of the (sub)domains and can even have them signed with a
Certificate Authority (CA). It is evident that the underlying cause
of exploitable Dares is the lack of authenticity checking for the
resources to which that DNS record points. We then propose three
defense mechanisms to effectively mitigate Dares with little human
effort.
Keywords
DNS; Dangling records; Domain hijacking
1.
|,Data
06547098.pdf,|Existing
fine-grained,
dynamic
information-flow control assume that
is acceptable to
terminate the entire system when an incorrect flow is
detectedi.e,
they give up availability for the sake of
confidentiality and integrity. This is an unrealistic limitation
for systems such as long-running servers.
We identify public labels and delayed exceptions as crucial
ingredients for making information-flow errors recoverable in
a sound and usable language, and we propose two new error-
handling mechanisms that make all errors recoverable. The
first mechanism builds directly on these basic ingredients,
using not-a-values (NaVs) and data flow to propagate errors.
The second mechanism adapts the standard exception model
to satisfy the extra constraints arising from information flow
control, converting thrown exceptions to delayed ones at certain
points. We prove that both mechanisms enjoy the fundamental
soundness property of non-interference. Finally, we describe
a prototype implementation of a full-scale language with
NaVs and report on our experience building robust software
components in this setting.
Keywords-dynamic information flow control, fine-grained
labeling, availability, reliability, error recovery, exception han-
dling, programming-language design, public labels, delayed
exceptions, not-a-values, NaVs
|,Non-data
06547109.pdf,|This paper presents a novel mechanism, called
Ally Friendly Jamming, which aims at providing an intelligent
jamming capability that can disable unauthorized (enemy)
wireless communication but at
the same time still allow
authorized wireless devices to communicate, even if all these
devices operate at the same frequency. The basic idea is to
jam the wireless channel continuously but properly control the
jamming signals with secret keys, so that the jamming signals
are unpredictable interference to unauthorized devices, but are
recoverable by authorized ones equipped with the secret keys.
To achieve the ally friendly jamming capability, we develop
new techniques to generate ally jamming signals, to identify
and synchronize with multiple ally jammers. This paper
also reports the analysis, implementation, and experimental
evaluation of ally friendly jamming on a software defined
radio platform. Both the analytical and experimental results
indicate that the proposed techniques can effectively disable
enemy wireless communication and at the same time maintain
wireless communication between authorized devices.
Keywords-Wireless; friendly jamming; interference cancella-
tion
I. |,Non-data
p553-dimitrov.pdf,|
This paper extends the choice available for secure real num-
ber implementations with two new contributions. We will
consider the numbers represented in form a b where
is the golden ratio, and in form (1)s 2e where e is a
fixed-point number. We develop basic arithmetic operations
together with some frequently used elementary functions.
All the operations are implemented and benchmarked on
Sharemind secure multi-party computation framework. It
turns out that the new proposals provide viable alternatives
to standard floating- and fixed-point implementations from
the performance/error viewpoint in various settings. How-
ever, the optimal choice still depends on the exact require-
ments of the numerical algorithm to be implemented.
Keywords
Secure fixed- and floating-point arithmetic, privacy-preserving
data analysis, secure computations
1.
|,Non-data
p418-kumaresan.pdf,|
Motivated by the impossibility of achieving fairness in secure com-
putation [Cleve, STOC 1986], recent works study a model of fair-
ness in which an adversarial party that aborts on receiving output is
forced to pay a mutually predefined monetary penalty to every other
party that did not receive the output. These works show how to de-
sign protocols for secure computation with penalties that guaran-
tees that either fairness is guaranteed or that each honest party ob-
tains a monetary penalty from the adversary. Protocols for this task
are typically designed in an hybrid model where parties have access
to a claim-or-refund transaction functionality denote F
In this work, we obtain improvements on the efficiency of these
constructions by amortizing the cost over multiple executions of
secure computation with penalties. More precisely, for computa-
tional security parameter , we design a protocol that implements
(cid:96) = poly() instances of secure computation with penalties where
the total number of calls to F
Keywords: Secure computation, fairness, Bitcoin, amortization.
CR is independent of (cid:96).
CR.
1.
|,Non-data
a0b46f4325d28d6e02f4ebbf70f40a1263a5.pdf,|
The results of botnet detection methods are usually presented without any comparison. Although
it is generally accepted that more comparisons with third-party methods may help to improve the
area, few papers could do it. Among the factors that prevent a comparison are the difficulties to
share a dataset, the lack of a good dataset, the absence of a proper description of the methods and
the lack of a comparison methodology. This paper compares the output of three different botnet
detection methods by executing them over a new, real, labeled and large botnet dataset. This
dataset includes botnet, normal and background traffic. The results of our two methods (BClus
and CAMNEP) and BotHunter were compared using a methodology and a novel error metric
designed for botnet detections methods. We conclude that comparing methods indeed helps to
better estimate how good the methods are, to improve the algorithms, to build better datasets and
to build a comparison methodology.
Keywords: Botnet detection, Malware detection, Methods comparison, Botnet dataset, Anomaly
detection, Network traffic
1. |,Data
p1216-yang.pdf,|
Mnemonic strategy has been recommended to help users generate
secure and memorable passwords. We evaluated the security of 6
mnemonic strategy variants in a series of online studies involving
5, 484 participants. In addition to applying the standard method
of using guess numbers or similar metrics to compare the gen-
erated passwords, we also measured the frequencies of the most
commonly chosen sentences as well as the resulting passwords.
While metrics similar to guess numbers suggested that all variants
provided highly secure passwords, statistical metrics told a differ-
ent story.
In particular, differences in the exact instructions had
a tremendous impact on the security level of the resulting pass-
words. We examined the mental workload and memorability of 2
mnemonic strategy variants in another online study with 752 par-
ticipants. Although perceived workloads for the mnemonic strategy
variants were higher than that for the control group where no strat-
egy is required, no significant reduction in password recall after 1
week was obtained.
1.
|,Data
06547127.pdf,|Order-preserving
encryption
scheme where the sort order of ciphertexts matches the sort
order of the corresponding plaintextsallows databases and
other applications to process queries involving order over
encrypted data efficiently. The ideal security guarantee for
order-preserving encryption put forth in the literature is for
the ciphertexts to reveal no information about the plaintexts
besides order. Even though more than a dozen schemes were
proposed, all these schemes leak more information than order.
This paper presents the first order-preserving scheme that
achieves ideal security. Our main technique is mutable cipher-
texts, meaning that over time, the ciphertexts for a small
number of plaintext values change, and we prove that mutable
ciphertexts are needed for ideal security. Our resulting protocol
is interactive, with a small number of interactions.
We implemented our scheme and evaluated it on mi-