10,414 research outputs found
ObliviSync: Practical Oblivious File Backup and Synchronization
Oblivious RAM (ORAM) protocols are powerful techniques that hide a client's
data as well as access patterns from untrusted service providers. We present an
oblivious cloud storage system, ObliviSync, that specifically targets one of
the most widely-used personal cloud storage paradigms: synchronization and
backup services, popular examples of which are Dropbox, iCloud Drive, and
Google Drive. This setting provides a unique opportunity because the above
privacy properties can be achieved with a simpler form of ORAM called
write-only ORAM, which allows for dramatically increased efficiency compared to
related work. Our solution is asymptotically optimal and practically efficient,
with a small constant overhead of approximately 4x compared with non-private
file storage, depending only on the total data size and parameters chosen
according to the usage rate, and not on the number or size of individual files.
Our construction also offers protection against timing-channel attacks, which
has not been previously considered in ORAM protocols. We built and evaluated a
full implementation of ObliviSync that supports multiple simultaneous read-only
clients and a single concurrent read/write client whose edits automatically and
seamlessly propagate to the readers. We show that our system functions under
high work loads, with realistic file size distributions, and with small
additional latency (as compared to a baseline encrypted file system) when
paired with Dropbox as the synchronization service.Comment: 15 pages. Accepted to NDSS 201
An Information-Theoretic Test for Dependence with an Application to the Temporal Structure of Stock Returns
Information theory provides ideas for conceptualising information and
measuring relationships between objects. It has found wide application in the
sciences, but economics and finance have made surprisingly little use of it. We
show that time series data can usefully be studied as information -- by noting
the relationship between statistical redundancy and dependence, we are able to
use the results of information theory to construct a test for joint dependence
of random variables. The test is in the same spirit of those developed by
Ryabko and Astola (2005, 2006b,a), but differs from these in that we add extra
randomness to the original stochatic process. It uses data compression to
estimate the entropy rate of a stochastic process, which allows it to measure
dependence among sets of random variables, as opposed to the existing
econometric literature that uses entropy and finds itself restricted to
pairwise tests of dependence. We show how serial dependence may be detected in
S&P500 and PSI20 stock returns over different sample periods and frequencies.
We apply the test to synthetic data to judge its ability to recover known
temporal dependence structures.Comment: 22 pages, 7 figure
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