1,064 research outputs found
Distance Sensitive Bloom Filters Without False Negatives
A Bloom filter is a widely used data-structure for representing a set and
answering queries of the form "Is in ?". By allowing some false positive
answers (saying "yes" when the answer is in fact `no') Bloom filters use space
significantly below what is required for storing . In the distance sensitive
setting we work with a set of (Hamming) vectors and seek a data structure
that offers a similar trade-off, but answers queries of the form "Is close
to an element of ?" (in Hamming distance). Previous work on distance
sensitive Bloom filters have accepted false positive and false negative
answers. Absence of false negatives is of critical importance in many
applications of Bloom filters, so it is natural to ask if this can be also
achieved in the distance sensitive setting. Our main contributions are upper
and lower bounds (that are tight in several cases) for space usage in the
distance sensitive setting where false negatives are not allowed.Comment: Published in SODA 201
Retouched Bloom Filters: Allowing Networked Applications to Flexibly Trade Off False Positives Against False Negatives
Where distributed agents must share voluminous set membership information,
Bloom filters provide a compact, though lossy, way for them to do so. Numerous
recent networking papers have examined the trade-offs between the bandwidth
consumed by the transmission of Bloom filters, and the error rate, which takes
the form of false positives, and which rises the more the filters are
compressed. In this paper, we introduce the retouched Bloom filter (RBF), an
extension that makes the Bloom filter more flexible by permitting the removal
of selected false positives at the expense of generating random false
negatives. We analytically show that RBFs created through a random process
maintain an overall error rate, expressed as a combination of the false
positive rate and the false negative rate, that is equal to the false positive
rate of the corresponding Bloom filters. We further provide some simple
heuristics and improved algorithms that decrease the false positive rate more
than than the corresponding increase in the false negative rate, when creating
RBFs. Finally, we demonstrate the advantages of an RBF over a Bloom filter in a
distributed network topology measurement application, where information about
large stop sets must be shared among route tracing monitors.Comment: This is a new version of the technical reports with improved
algorithms and theorical analysis of algorithm
In-packet Bloom filters: Design and networking applications
The Bloom filter (BF) is a well-known space-efficient data structure that
answers set membership queries with some probability of false positives. In an
attempt to solve many of the limitations of current inter-networking
architectures, some recent proposals rely on including small BFs in packet
headers for routing, security, accountability or other purposes that move
application states into the packets themselves. In this paper, we consider the
design of such in-packet Bloom filters (iBF). Our main contributions are
exploring the design space and the evaluation of a series of extensions (1) to
increase the practicality and performance of iBFs, (2) to enable
false-negative-free element deletion, and (3) to provide security enhancements.
In addition to the theoretical estimates, extensive simulations of the multiple
design parameters and implementation alternatives validate the usefulness of
the extensions, providing for enhanced and novel iBF networking applications.Comment: 15 pages, 11 figures, preprint submitted to Elsevier COMNET Journa
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