Receiver-oriented design of Bloom filters for data-centric routing

Guo, Deke; He, Yuan; Yang, Panlong

doi:10.1016/j.comnet.2009.10.002

Cited by 14 publications

(6 citation statements)

References 22 publications

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“…The observed distribution of outputs within an experiment, measured as the Hamming distance between output bit vectors (BV), was very close to the mean value of m/2 bits (128) with a small standard deviation. 4 The observed average number of bits set and their distribution were comparable to standard iBF constructs. Additionally, we analyzed whether the 20 most frequent bit positions set in secure iBFs corresponded to bits set in plain iBFs.…”

Section: Securitymentioning

confidence: 60%

“…Now, we allow a different number of bits k per candidate. For instance, with d = 8 the distribution of k among the candidates is [4,4,5,5,6,6,7,7]. Intuitively, this naming scheme adapts better to the total number of elements in the iBF (k closer to k opt = ln(2) * m n ).…”

Section: Distribution Of the Number Of Hash Functions (K)mentioning

confidence: 99%

“…Since the seminal survey work by [1], Bloom filters (BF) [2] have increasingly become a fundamental data aggregation component to address performance and scalability issues of very diverse network applications, including overlay networks [3], data-centric routing [4], traffic monitoring, and so on. In this work, we focus on the subset of distributed networking applications based on packet-header-size Bloom filters to share some state (information set) among network nodes.…”

Section: Introductionmentioning

confidence: 99%

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In-packet Bloom filters: Design and networking applications

et al. 2011

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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 inpacket 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.

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Section: Securitymentioning

confidence: 60%

Section: Distribution Of the Number Of Hash Functions (K)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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In-packet Bloom filters: Design and networking applications

et al. 2011

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“…In this way, we can answer a membership query for any object s i , and a user can check whether all bits h j .s i / are set to 1 for 1 j k. If not, SBF returns "false", i.e., s i is not a member of S. Otherwise, we assume that s i is a member of S . Due to hash collisions, an SBF may yield a false positive if it wrongly identifies an object s i as belonging to S. The cause is that all bits at SBFOEh j .s i / for 1 j k have been set to one by other objects in S [12] . The false positive probability can be theoretically derived as follows [13,14] :…”

Section: Sbf-based Set Reconciliation Methodsmentioning

confidence: 99%

Comparing set reconciliation methods based on bloom filters and their variants

Teng

Guo

et al. 2016

Tinshhua Sci. Technol.

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Set reconciliation between two nodes is widely used in network applications. The basic idea is that each member of a node pair has an object set and seeks to deliver its unique objects to the other member. The Standard Bloom Filter (SBF) and its variants, such as the Invertible Bloom Filter (IBF), are effective approaches to solving the set reconciliation problem. The SBF-based method requires each node to represent its objects using an SBF, which is exchanged with the other node. A receiving node queries the received SBF against its local objects to identify the unique objects. Finally, each node exchanges its unique objects with the other node in the node pair. For the IBFbased method, each node represents its objects using an IBF, which is then exchanged. A receiving node subtracts the received IBF from its local IBF so as to decode the different objects between the two sets. Intuitively, it would seem that the IBF-based method, with only one round of communication, entails less communication overhead than the SBF-based method, which incurs two rounds of communication. Our research results, however, indicate that neither of these two methods has an absolute advantages over the others. In this paper, we aim to provide an in-depth understanding of the two methods, by evaluating and comparing their communication overhead. We find that the best method depends on parameter settings. We demonstrate that the SBF-based method outperforms the IBF-based method in most cases. But when the number of different objects in the two sets is below a certain threshold, the IBF-based method outperforms the SBF-based method.

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“…Hitherto, the applications of BF and its variants are manyfold. In the field of networking, BF has been employed to enable routing and forwarding [2] [3] [4] [5] [6] [7] [8], web caching [9] [10], network monitoring [11], security enhancement [12] [13], content delivering [14], etc. In the area of databases, BF is a proper option to support query and search [15] [16], privacy preservation [17], key-value store [18] [19], content synchronization [20] [21] [22] [23] [24], duplicate detection [25] and so on.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

Luo

Guo

Richard

et al. 2019

IEEE Commun. Surv. Tutorials

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178

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Bloom filter (BF) has been widely used to support membership query, i.e., to judge whether a given element x is a member of a given set S or not. Recent years have seen a flourish design explosion of BF due to its characteristic of space-efficiency and the functionality of constant-time membership query. The existing reviews or surveys mainly focus on the applications of BF, but fall short in covering the current trends, thereby lacking intrinsic understanding of their design philosophy. To this end, this survey provides an overview of BF and its variants, with an emphasis on the optimization techniques. Basically, we survey the existing variants from two dimensions, i.e., performance and generalization. To improve the performance, dozens of variants devote themselves to reducing the false positives and implementation costs. Besides, tens of variants generalize the BF framework in more scenarios by diversifying the input sets and enriching the output functionalities. To summarize the existing efforts, we conduct an in-depth study of the existing literature on BF optimization, covering more than 60 variants. We unearth the design philosophy of these variants and elaborate how the employed optimization techniques improve BF. Furthermore, comprehensive analysis and qualitative comparison are conducted from the perspectives of BF components. Lastly, we highlight the future trends of designing BFs. This is, to the best of our knowledge, the first survey that accomplishes such goals.

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Receiver-oriented design of Bloom filters for data-centric routing

Cited by 14 publications

References 22 publications

In-packet Bloom filters: Design and networking applications

In-packet Bloom filters: Design and networking applications

Comparing set reconciliation methods based on bloom filters and their variants

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

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