8,282 research outputs found
Chaotic Compilation for Encrypted Computing: Obfuscation but Not in Name
An `obfuscation' for encrypted computing is quantified exactly here, leading
to an argument that security against polynomial-time attacks has been achieved
for user data via the deliberately `chaotic' compilation required for security
properties in that environment. Encrypted computing is the emerging science and
technology of processors that take encrypted inputs to encrypted outputs via
encrypted intermediate values (at nearly conventional speeds). The aim is to
make user data in general-purpose computing secure against the operator and
operating system as potential adversaries. A stumbling block has always been
that memory addresses are data and good encryption means the encrypted value
varies randomly, and that makes hitting any target in memory problematic
without address decryption, yet decryption anywhere on the memory path would
open up many easily exploitable vulnerabilities. This paper `solves (chaotic)
compilation' for processors without address decryption, covering all of ANSI C
while satisfying the required security properties and opening up the field for
the standard software tool-chain and infrastructure. That produces the argument
referred to above, which may also hold without encryption.Comment: 31 pages. Version update adds "Chaotic" in title and throughout
paper, and recasts abstract and Intro and other sections of the text for
better access by cryptologists. To the same end it introduces the polynomial
time defense argument explicitly in the final section, having now set that
denouement out in the abstract and intr
Schema Independent Relational Learning
Learning novel concepts and relations from relational databases is an
important problem with many applications in database systems and machine
learning. Relational learning algorithms learn the definition of a new relation
in terms of existing relations in the database. Nevertheless, the same data set
may be represented under different schemas for various reasons, such as
efficiency, data quality, and usability. Unfortunately, the output of current
relational learning algorithms tends to vary quite substantially over the
choice of schema, both in terms of learning accuracy and efficiency. This
variation complicates their off-the-shelf application. In this paper, we
introduce and formalize the property of schema independence of relational
learning algorithms, and study both the theoretical and empirical dependence of
existing algorithms on the common class of (de) composition schema
transformations. We study both sample-based learning algorithms, which learn
from sets of labeled examples, and query-based algorithms, which learn by
asking queries to an oracle. We prove that current relational learning
algorithms are generally not schema independent. For query-based learning
algorithms we show that the (de) composition transformations influence their
query complexity. We propose Castor, a sample-based relational learning
algorithm that achieves schema independence by leveraging data dependencies. We
support the theoretical results with an empirical study that demonstrates the
schema dependence/independence of several algorithms on existing benchmark and
real-world datasets under (de) compositions
A Survey on Array Storage, Query Languages, and Systems
Since scientific investigation is one of the most important providers of
massive amounts of ordered data, there is a renewed interest in array data
processing in the context of Big Data. To the best of our knowledge, a unified
resource that summarizes and analyzes array processing research over its long
existence is currently missing. In this survey, we provide a guide for past,
present, and future research in array processing. The survey is organized along
three main topics. Array storage discusses all the aspects related to array
partitioning into chunks. The identification of a reduced set of array
operators to form the foundation for an array query language is analyzed across
multiple such proposals. Lastly, we survey real systems for array processing.
The result is a thorough survey on array data storage and processing that
should be consulted by anyone interested in this research topic, independent of
experience level. The survey is not complete though. We greatly appreciate
pointers towards any work we might have forgotten to mention.Comment: 44 page
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