450 research outputs found
Statistically-secure ORAM with Overhead
We demonstrate a simple, statistically secure, ORAM with computational
overhead ; previous ORAM protocols achieve only
computational security (under computational assumptions) or require
overheard. An additional benefit of our ORAM is its
conceptual simplicity, which makes it easy to implement in both software and
(commercially available) hardware.
Our construction is based on recent ORAM constructions due to Shi, Chan,
Stefanov, and Li (Asiacrypt 2011) and Stefanov and Shi (ArXiv 2012), but with
some crucial modifications in the algorithm that simplifies the ORAM and enable
our analysis. A central component in our analysis is reducing the analysis of
our algorithm to a "supermarket" problem; of independent interest (and of
importance to our analysis,) we provide an upper bound on the rate of "upset"
customers in the "supermarket" problem
Balanced Allocations and Double Hashing
Double hashing has recently found more common usage in schemes that use
multiple hash functions. In double hashing, for an item , one generates two
hash values and , and then uses combinations for to generate multiple hash values from the initial two. We
first perform an empirical study showing that, surprisingly, the performance
difference between double hashing and fully random hashing appears negligible
in the standard balanced allocation paradigm, where each item is placed in the
least loaded of choices, as well as several related variants. We then
provide theoretical results that explain the behavior of double hashing in this
context.Comment: Further updated, small improvements/typos fixe
Data Sketches for Disaggregated Subset Sum and Frequent Item Estimation
We introduce and study a new data sketch for processing massive datasets. It
addresses two common problems: 1) computing a sum given arbitrary filter
conditions and 2) identifying the frequent items or heavy hitters in a data
set. For the former, the sketch provides unbiased estimates with state of the
art accuracy. It handles the challenging scenario when the data is
disaggregated so that computing the per unit metric of interest requires an
expensive aggregation. For example, the metric of interest may be total clicks
per user while the raw data is a click stream with multiple rows per user. Thus
the sketch is suitable for use in a wide range of applications including
computing historical click through rates for ad prediction, reporting user
metrics from event streams, and measuring network traffic for IP flows.
We prove and empirically show the sketch has good properties for both the
disaggregated subset sum estimation and frequent item problems. On i.i.d. data,
it not only picks out the frequent items but gives strongly consistent
estimates for the proportion of each frequent item. The resulting sketch
asymptotically draws a probability proportional to size sample that is optimal
for estimating sums over the data. For non i.i.d. data, we show that it
typically does much better than random sampling for the frequent item problem
and never does worse. For subset sum estimation, we show that even for
pathological sequences, the variance is close to that of an optimal sampling
design. Empirically, despite the disadvantage of operating on disaggregated
data, our method matches or bests priority sampling, a state of the art method
for pre-aggregated data and performs orders of magnitude better on skewed data
compared to uniform sampling. We propose extensions to the sketch that allow it
to be used in combining multiple data sets, in distributed systems, and for
time decayed aggregation
Local cluster aggregation models of explosive percolation
We introduce perhaps the simplest models of graph evolution with choice that
demonstrate discontinuous percolation transitions and can be analyzed via
mathematical evolution equations. These models are local, in the sense that at
each step of the process one edge is selected from a small set of potential
edges sharing common vertices and added to the graph. We show that the
evolution can be accurately described by a system of differential equations and
that such models exhibit the discontinuous emergence of the giant component.
Yet, they also obey scaling behaviors characteristic of continuous transitions,
with scaling exponents that differ from the classic Erdos-Renyi model.Comment: Final version as appearing in PR
Social-Aware Forwarding Improves Routing Performance in Pocket Switched Networks
Several social-aware forwarding strategies have been recently introduced in
opportunistic networks, and proved effective in considerably in- creasing
routing performance through extensive simulation studies based on real-world
data. However, this performance improvement comes at the expense of storing a
considerable amount of state information (e.g, history of past encounters) at
the nodes. Hence, whether the benefits on routing performance comes directly
from the social-aware forwarding mechanism, or indirectly by the fact state
information is exploited is not clear. Thus, the question of whether
social-aware forwarding by itself is effective in improving opportunistic
network routing performance remained unaddressed so far. In this paper, we give
a first, positive answer to the above question, by investigating the expected
message delivery time as the size of the net- work grows larger
Speed-up via Quantum Sampling
The Markov Chain Monte Carlo method is at the heart of efficient
approximation schemes for a wide range of problems in combinatorial enumeration
and statistical physics. It is therefore very natural and important to
determine whether quantum computers can speed-up classical mixing processes
based on Markov chains. To this end, we present a new quantum algorithm, making
it possible to prepare a quantum sample, i.e., a coherent version of the
stationary distribution of a reversible Markov chain. Our algorithm has a
significantly better running time than that of a previous algorithm based on
adiabatic state generation. We also show that our methods provide a speed-up
over a recently proposed method for obtaining ground states of (classical)
Hamiltonians.Comment: 8 pages, fixed some minor typo
Adaptive cuckoo filters
We introduce the adaptive cuckoo filter (ACF), a data structure for approximate set membership that extends
cuckoo filters by reacting to false positives, removing them for future queries. As an example application,
in packet processing queries may correspond to flow identifiers, so a search for an element is likely to be
followed by repeated searches for that element. Removing false positives can therefore significantly lower
the false-positive rate. The ACF, like the cuckoo filter, uses a cuckoo hash table to store fingerprints. We allow
fingerprint entries to be changed in response to a false positive in a manner designed to minimize the effect
on the performance of the filter. We show that the ACF is able to significantly reduce the false-positive rate
by presenting both a theoretical model for the false-positive rate and simulations using both synthetic data
sets and real packet trace
Models and Algorithms for Graph Watermarking
We introduce models and algorithmic foundations for graph watermarking. Our
frameworks include security definitions and proofs, as well as
characterizations when graph watermarking is algorithmically feasible, in spite
of the fact that the general problem is NP-complete by simple reductions from
the subgraph isomorphism or graph edit distance problems. In the digital
watermarking of many types of files, an implicit step in the recovery of a
watermark is the mapping of individual pieces of data, such as image pixels or
movie frames, from one object to another. In graphs, this step corresponds to
approximately matching vertices of one graph to another based on graph
invariants such as vertex degree. Our approach is based on characterizing the
feasibility of graph watermarking in terms of keygen, marking, and
identification functions defined over graph families with known distributions.
We demonstrate the strength of this approach with exemplary watermarking
schemes for two random graph models, the classic Erd\H{o}s-R\'{e}nyi model and
a random power-law graph model, both of which are used to model real-world
networks
- …