2,166 research outputs found
Conclave: secure multi-party computation on big data (extended TR)
Secure Multi-Party Computation (MPC) allows mutually distrusting parties to
run joint computations without revealing private data. Current MPC algorithms
scale poorly with data size, which makes MPC on "big data" prohibitively slow
and inhibits its practical use.
Many relational analytics queries can maintain MPC's end-to-end security
guarantee without using cryptographic MPC techniques for all operations.
Conclave is a query compiler that accelerates such queries by transforming them
into a combination of data-parallel, local cleartext processing and small MPC
steps. When parties trust others with specific subsets of the data, Conclave
applies new hybrid MPC-cleartext protocols to run additional steps outside of
MPC and improve scalability further.
Our Conclave prototype generates code for cleartext processing in Python and
Spark, and for secure MPC using the Sharemind and Obliv-C frameworks. Conclave
scales to data sets between three and six orders of magnitude larger than
state-of-the-art MPC frameworks support on their own. Thanks to its hybrid
protocols, Conclave also substantially outperforms SMCQL, the most similar
existing system.Comment: Extended technical report for EuroSys 2019 pape
On Expert Behaviors and Question Types for Efficient Query-Based Ontology Fault Localization
We challenge existing query-based ontology fault localization methods wrt.
assumptions they make, criteria they optimize, and interaction means they use.
We find that their efficiency depends largely on the behavior of the
interacting expert, that performed calculations can be inefficient or
imprecise, and that used optimization criteria are often not fully realistic.
As a remedy, we suggest a novel (and simpler) interaction approach which
overcomes all identified problems and, in comprehensive experiments on faulty
real-world ontologies, enables a successful fault localization while requiring
fewer expert interactions in 66 % of the cases, and always at least 80 % less
expert waiting time, compared to existing methods
Partitionning medical image databases for content-based queries on a grid
articleInternational audienceIn this article we study the impact of executing a medical image database query application on the grid. For lowering the total computation time, the image database is partitioned in subsets to be processed on different grid nodes. A theoretical model of the application computation cost and estimates of the grid execution overhead are used to efficiently partition the database. We show results demonstrating that smart partitioning of the database can lead to significant improvements in terms of total computation time
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