14,698 research outputs found
SqORAM: Read-Optimized Sequential Write-Only Oblivious RAM
Oblivious RAM protocols (ORAMs) allow a client to access data from an
untrusted storage device without revealing the access patterns. Typically, the
ORAM adversary can observe both read and write accesses. Write-only ORAMs
target a more practical, {\em multi-snapshot adversary} only monitoring client
writes -- typical for plausible deniability and censorship-resilient systems.
This allows write-only ORAMs to achieve significantly-better asymptotic
performance. However, these apparent gains do not materialize in real
deployments primarily due to the random data placement strategies used to break
correlations between logical and physical namespaces, a required property for
write access privacy. Random access performs poorly on both rotational disks
and SSDs (often increasing wear significantly, and interfering with
wear-leveling mechanisms). In this work, we introduce SqORAM, a new
locality-preserving write-only ORAM that preserves write access privacy without
requiring random data access. Data blocks close to each other in the logical
domain land in close proximity on the physical media. Importantly, SqORAM
maintains this data locality property over time, significantly increasing read
throughput. A full Linux kernel-level implementation of SqORAM is 100x faster
than non locality-preserving solutions for standard workloads and is 60-100%
faster than the state-of-the-art for typical file system workloads
Graph-Sparse LDA: A Topic Model with Structured Sparsity
Originally designed to model text, topic modeling has become a powerful tool
for uncovering latent structure in domains including medicine, finance, and
vision. The goals for the model vary depending on the application: in some
cases, the discovered topics may be used for prediction or some other
downstream task. In other cases, the content of the topic itself may be of
intrinsic scientific interest.
Unfortunately, even using modern sparse techniques, the discovered topics are
often difficult to interpret due to the high dimensionality of the underlying
space. To improve topic interpretability, we introduce Graph-Sparse LDA, a
hierarchical topic model that leverages knowledge of relationships between
words (e.g., as encoded by an ontology). In our model, topics are summarized by
a few latent concept-words from the underlying graph that explain the observed
words. Graph-Sparse LDA recovers sparse, interpretable summaries on two
real-world biomedical datasets while matching state-of-the-art prediction
performance
Tolerating Correlated Failures in Massively Parallel Stream Processing Engines
Fault-tolerance techniques for stream processing engines can be categorized
into passive and active approaches. A typical passive approach periodically
checkpoints a processing task's runtime states and can recover a failed task by
restoring its runtime state using its latest checkpoint. On the other hand, an
active approach usually employs backup nodes to run replicated tasks. Upon
failure, the active replica can take over the processing of the failed task
with minimal latency. However, both approaches have their own inadequacies in
Massively Parallel Stream Processing Engines (MPSPE). The passive approach
incurs a long recovery latency especially when a number of correlated nodes
fail simultaneously, while the active approach requires extra replication
resources. In this paper, we propose a new fault-tolerance framework, which is
Passive and Partially Active (PPA). In a PPA scheme, the passive approach is
applied to all tasks while only a selected set of tasks will be actively
replicated. The number of actively replicated tasks depends on the available
resources. If tasks without active replicas fail, tentative outputs will be
generated before the completion of the recovery process. We also propose
effective and efficient algorithms to optimize a partially active replication
plan to maximize the quality of tentative outputs. We implemented PPA on top of
Storm, an open-source MPSPE and conducted extensive experiments using both real
and synthetic datasets to verify the effectiveness of our approach
Modeling Big Medical Survival Data Using Decision Tree Analysis with Apache Spark
In many medical studies, an outcome of interest is not only whether an event occurred, but when an event occurred; and an example of this is Alzheimer’s disease (AD). Identifying patients with Mild Cognitive Impairment (MCI) who are likely to develop Alzheimer’s disease (AD) is highly important for AD treatment. Previous studies suggest that not all MCI patients will convert to AD. Massive amounts of data from longitudinal and extensive studies on thousands of Alzheimer’s patients have been generated. Building a computational model that can predict conversion form MCI to AD can be highly beneficial for early intervention and treatment planning for AD. This work presents a big data model that contains machine-learning techniques to determine the level of AD in a participant and predict the time of conversion to AD. The proposed framework considers one of the widely used screening assessment for detecting cognitive impairment called Montreal Cognitive Assessment (MoCA). MoCA data set was collected from different centers and integrated into our large data framework storage using a Hadoop Data File System (HDFS); the data was then analyzed using an Apache Spark framework. The accuracy of the proposed framework was compared with a semi-parametric Cox survival analysis model
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