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ScalaTrace: Tracing, Analysis and Modeling of HPC Codes at Scale
Characterizing the communication behavior of large-scale applications is a difficult and costly task due to code/system complexity and their long execution times. An alternative to running actual codes is to gather their communication traces and then replay them, which facilitates application tuning and future procurements. While past approaches lacked lossless scalable trace collection, we contribute an approach that provides orders of magnitude smaller, if not near constant-size, communication traces regardless of the number of nodes while preserving structural information. We introduce intra- and inter-node compression techniques of MPI events, we develop a scheme to preserve time and causality of communication events, and we present results of our implementation for BlueGene/L. Given this novel capability, we discuss its impact on communication tuning and on trace extrapolation. To the best of our knowledge, such a concise representation of MPI traces in a scalable manner combined with time-preserving deterministic MPI call replay are without any precedence
SECMACE: Scalable and Robust Identity and Credential Management Infrastructure in Vehicular Communication Systems
Several years of academic and industrial research efforts have converged to a
common understanding on fundamental security building blocks for the upcoming
Vehicular Communication (VC) systems. There is a growing consensus towards
deploying a special-purpose identity and credential management infrastructure,
i.e., a Vehicular Public-Key Infrastructure (VPKI), enabling pseudonymous
authentication, with standardization efforts towards that direction. In spite
of the progress made by standardization bodies (IEEE 1609.2 and ETSI) and
harmonization efforts (Car2Car Communication Consortium (C2C-CC)), significant
questions remain unanswered towards deploying a VPKI. Deep understanding of the
VPKI, a central building block of secure and privacy-preserving VC systems, is
still lacking. This paper contributes to the closing of this gap. We present
SECMACE, a VPKI system, which is compatible with the IEEE 1609.2 and ETSI
standards specifications. We provide a detailed description of our
state-of-the-art VPKI that improves upon existing proposals in terms of
security and privacy protection, and efficiency. SECMACE facilitates
multi-domain operations in the VC systems and enhances user privacy, notably
preventing linking pseudonyms based on timing information and offering
increased protection even against honest-but-curious VPKI entities. We propose
multiple policies for the vehicle-VPKI interactions, based on which and two
large-scale mobility trace datasets, we evaluate the full-blown implementation
of SECMACE. With very little attention on the VPKI performance thus far, our
results reveal that modest computing resources can support a large area of
vehicles with very low delays and the most promising policy in terms of privacy
protection can be supported with moderate overhead.Comment: 14 pages, 9 figures, 10 tables, IEEE Transactions on Intelligent
Transportation System
Preserving Both Privacy and Utility in Network Trace Anonymization
As network security monitoring grows more sophisticated, there is an
increasing need for outsourcing such tasks to third-party analysts. However,
organizations are usually reluctant to share their network traces due to
privacy concerns over sensitive information, e.g., network and system
configuration, which may potentially be exploited for attacks. In cases where
data owners are convinced to share their network traces, the data are typically
subjected to certain anonymization techniques, e.g., CryptoPAn, which replaces
real IP addresses with prefix-preserving pseudonyms. However, most such
techniques either are vulnerable to adversaries with prior knowledge about some
network flows in the traces, or require heavy data sanitization or
perturbation, both of which may result in a significant loss of data utility.
In this paper, we aim to preserve both privacy and utility through shifting the
trade-off from between privacy and utility to between privacy and computational
cost. The key idea is for the analysts to generate and analyze multiple
anonymized views of the original network traces; those views are designed to be
sufficiently indistinguishable even to adversaries armed with prior knowledge,
which preserves the privacy, whereas one of the views will yield true analysis
results privately retrieved by the data owner, which preserves the utility. We
present the general approach and instantiate it based on CryptoPAn. We formally
analyze the privacy of our solution and experimentally evaluate it using real
network traces provided by a major ISP. The results show that our approach can
significantly reduce the level of information leakage (e.g., less than 1\% of
the information leaked by CryptoPAn) with comparable utility
Privacy-Friendly Mobility Analytics using Aggregate Location Data
Location data can be extremely useful to study commuting patterns and
disruptions, as well as to predict real-time traffic volumes. At the same time,
however, the fine-grained collection of user locations raises serious privacy
concerns, as this can reveal sensitive information about the users, such as,
life style, political and religious inclinations, or even identities. In this
paper, we study the feasibility of crowd-sourced mobility analytics over
aggregate location information: users periodically report their location, using
a privacy-preserving aggregation protocol, so that the server can only recover
aggregates -- i.e., how many, but not which, users are in a region at a given
time. We experiment with real-world mobility datasets obtained from the
Transport For London authority and the San Francisco Cabs network, and present
a novel methodology based on time series modeling that is geared to forecast
traffic volumes in regions of interest and to detect mobility anomalies in
them. In the presence of anomalies, we also make enhanced traffic volume
predictions by feeding our model with additional information from correlated
regions. Finally, we present and evaluate a mobile app prototype, called
Mobility Data Donors (MDD), in terms of computation, communication, and energy
overhead, demonstrating the real-world deployability of our techniques.Comment: Published at ACM SIGSPATIAL 201
Crux: Locality-Preserving Distributed Services
Distributed systems achieve scalability by distributing load across many
machines, but wide-area deployments can introduce worst-case response latencies
proportional to the network's diameter. Crux is a general framework to build
locality-preserving distributed systems, by transforming an existing scalable
distributed algorithm A into a new locality-preserving algorithm ALP, which
guarantees for any two clients u and v interacting via ALP that their
interactions exhibit worst-case response latencies proportional to the network
latency between u and v. Crux builds on compact-routing theory, but generalizes
these techniques beyond routing applications. Crux provides weak and strong
consistency flavors, and shows latency improvements for localized interactions
in both cases, specifically up to several orders of magnitude for
weakly-consistent Crux (from roughly 900ms to 1ms). We deployed on PlanetLab
locality-preserving versions of a Memcached distributed cache, a Bamboo
distributed hash table, and a Redis publish/subscribe. Our results indicate
that Crux is effective and applicable to a variety of existing distributed
algorithms.Comment: 11 figure
Evaluating On-demand Pseudonym Acquisition Policies in Vehicular Communication Systems
Standardization and harmonization efforts have reached a consensus towards
using a special-purpose Vehicular Public-Key Infrastructure (VPKI) in upcoming
Vehicular Communication (VC) systems. However, there are still several
technical challenges with no conclusive answers; one such an important yet open
challenge is the acquisition of shortterm credentials, pseudonym: how should
each vehicle interact with the VPKI, e.g., how frequently and for how long?
Should each vehicle itself determine the pseudonym lifetime? Answering these
questions is far from trivial. Each choice can affect both the user privacy and
the system performance and possibly, as a result, its security. In this paper,
we make a novel systematic effort to address this multifaceted question. We
craft three generally applicable policies and experimentally evaluate the VPKI
system performance, leveraging two large-scale mobility datasets. We consider
the most promising, in terms of efficiency, pseudonym acquisition policies; we
find that within this class of policies, the most promising policy in terms of
privacy protection can be supported with moderate overhead. Moreover, in all
cases, this work is the first to provide tangible evidence that the
state-of-the-art VPKI can serve sizable areas or domain with modest computing
resources.Comment: 6 pages, 7 figures, IoV-VoI'1
Privacy-Friendly Collaboration for Cyber Threat Mitigation
Sharing of security data across organizational boundaries has often been
advocated as a promising way to enhance cyber threat mitigation. However,
collaborative security faces a number of important challenges, including
privacy, trust, and liability concerns with the potential disclosure of
sensitive data. In this paper, we focus on data sharing for predictive
blacklisting, i.e., forecasting attack sources based on past attack
information. We propose a novel privacy-enhanced data sharing approach in which
organizations estimate collaboration benefits without disclosing their
datasets, organize into coalitions of allied organizations, and securely share
data within these coalitions. We study how different partner selection
strategies affect prediction accuracy by experimenting on a real-world dataset
of 2 billion IP addresses and observe up to a 105% prediction improvement.Comment: This paper has been withdrawn as it has been superseded by
arXiv:1502.0533
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