71,719 research outputs found
Undermining User Privacy on Mobile Devices Using AI
Over the past years, literature has shown that attacks exploiting the
microarchitecture of modern processors pose a serious threat to the privacy of
mobile phone users. This is because applications leave distinct footprints in
the processor, which can be used by malware to infer user activities. In this
work, we show that these inference attacks are considerably more practical when
combined with advanced AI techniques. In particular, we focus on profiling the
activity in the last-level cache (LLC) of ARM processors. We employ a simple
Prime+Probe based monitoring technique to obtain cache traces, which we
classify with Deep Learning methods including Convolutional Neural Networks. We
demonstrate our approach on an off-the-shelf Android phone by launching a
successful attack from an unprivileged, zeropermission App in well under a
minute. The App thereby detects running applications with an accuracy of 98%
and reveals opened websites and streaming videos by monitoring the LLC for at
most 6 seconds. This is possible, since Deep Learning compensates measurement
disturbances stemming from the inherently noisy LLC monitoring and unfavorable
cache characteristics such as random line replacement policies. In summary, our
results show that thanks to advanced AI techniques, inference attacks are
becoming alarmingly easy to implement and execute in practice. This once more
calls for countermeasures that confine microarchitectural leakage and protect
mobile phone applications, especially those valuing the privacy of their users
Instant restore after a media failure
Media failures usually leave database systems unavailable for several hours
until recovery is complete, especially in applications with large devices and
high transaction volume. Previous work introduced a technique called
single-pass restore, which increases restore bandwidth and thus substantially
decreases time to repair. Instant restore goes further as it permits read/write
access to any data on a device undergoing restore--even data not yet
restored--by restoring individual data segments on demand. Thus, the restore
process is guided primarily by the needs of applications, and the observed mean
time to repair is effectively reduced from several hours to a few seconds.
This paper presents an implementation and evaluation of instant restore. The
technique is incrementally implemented on a system starting with the
traditional ARIES design for logging and recovery. Experiments show that the
transaction latency perceived after a media failure can be cut down to less
than a second and that the overhead imposed by the technique on normal
processing is minimal. The net effect is that a few "nines" of availability are
added to the system using simple and low-overhead software techniques
Jaccard/Tanimoto similarity test and estimation methods
Binary data are used in a broad area of biological sciences. Using binary
presence-absence data, we can evaluate species co-occurrences that help
elucidate relationships among organisms and environments. To summarize
similarity between occurrences of species, we routinely use the
Jaccard/Tanimoto coefficient, which is the ratio of their intersection to their
union. It is natural, then, to identify statistically significant
Jaccard/Tanimoto coefficients, which suggest non-random co-occurrences of
species. However, statistical hypothesis testing using this similarity
coefficient has been seldom used or studied.
We introduce a hypothesis test for similarity for biological presence-absence
data, using the Jaccard/Tanimoto coefficient. Several key improvements are
presented including unbiased estimation of expectation and centered
Jaccard/Tanimoto coefficients, that account for occurrence probabilities. We
derived the exact and asymptotic solutions and developed the bootstrap and
measurement concentration algorithms to compute statistical significance of
binary similarity. Comprehensive simulation studies demonstrate that our
proposed methods produce accurate p-values and false discovery rates. The
proposed estimation methods are orders of magnitude faster than the exact
solution. The proposed methods are implemented in an open source R package
called jaccard (https://cran.r-project.org/package=jaccard).
We introduce a suite of statistical methods for the Jaccard/Tanimoto
similarity coefficient, that enable straightforward incorporation of
probabilistic measures in analysis for species co-occurrences. Due to their
generality, the proposed methods and implementations are applicable to a wide
range of binary data arising from genomics, biochemistry, and other areas of
science
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