Dual-load Bloom filter: Application for name lookup

Lee, Jungwon; Byun, Hayoung; Lim, Hyesook

doi:10.1016/j.comcom.2019.12.029

Cited by 15 publications

(10 citation statements)

References 28 publications

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“…The performance of packet forwarding is highly dependent on the speed of the name lookup and is determined by the efficiency of the LPM. For these reasons, many previous studies on packet forwarding in NDN focused on improving the speed of the name lookup by increasing the efficiency of LPM [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: Name Lookup In Ndn Packet Forwardingmentioning

confidence: 99%

“…Those techniques primarily focused on reducing the average number of memory accesses per lookup to speed up the name lookup. Alternatively, hashing techniques were used to achieve efficient name lookups [9][10][11][12][13][14][15], since a name consists of several components, each of which is a variable-length string. The hashing techniques are generally used to map variable-size data to a fixed-size value.…”

Section: Introductionmentioning

confidence: 99%

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Route Prefix Caching Using Bloom Filters in Named Data Networking

Kim

Shin

2020

Applied Sciences

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This paper proposes an elaborate route prefix caching scheme for fast packet forwarding in named data networking (NDN) which is a next-generation Internet structure. The name lookup is a crucial function of the NDN router, which delivers a packet based on its name rather than IP address. It carries out a complex process to find the longest matching prefix for the content name. Even the size of a name prefix is variable and unbounded; thus, the name lookup is to be more complicated and time-consuming. The name lookup can be sped up by using route prefix caching, but it may cause a problem when non-leaf prefixes are cached. The proposed prefix caching scheme can cache non-leaf prefixes, as well as leaf prefixes, without incurring any problem. For this purpose, a Bloom filter is kept for each prefix. The Bloom filter, which is widely used for checking membership, is utilized to indicate the branch information of a non-leaf prefix. The experimental result shows that the proposed caching scheme achieves a much higher hit ratio than other caching schemes. Furthermore, how much the parameters of the Bloom filter affect the cache miss count is quantitatively evaluated. The best performance can be achieved with merely 8-bit Bloom filters and two hash functions.

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Section: Name Lookup In Ndn Packet Forwardingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Route Prefix Caching Using Bloom Filters in Named Data Networking

Kim

Shin

2020

Applied Sciences

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“…A Bloom filter and its variant structures have been popularly used for many applications due to their simple and space-efficient nature identifying non-members of a given set [34][35][36][37][38][39][40].…”

Section: Bloom Filter Theorymentioning

confidence: 99%

Comparison on Search Failure between Hash Tables and a Functional Bloom Filter

Byun

Lim

2020

Applied Sciences

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Hash-based data structures have been widely used in many applications. An intrinsic problem of hashing is collision, in which two or more elements are hashed to the same value. If a hash table is heavily loaded, more collisions would occur. Elements that could not be stored in a hash table because of the collision cause search failures. Many variant structures have been studied to reduce the number of collisions, but none of the structures completely solves the collision problem. In this paper, we claim that a functional Bloom filter (FBF) provides a lower search failure rate than hash tables, when a hash table is heavily loaded. In other words, a hash table can be replaced with an FBF because the FBF is more effective than hash tables in the search failure rate in storing a large amount of data to a limited size of memory. While hash tables require to store each input key in addition to its return value, a functional Bloom filter stores return values without input keys, because different index combinations according to each input key can be used to identify the input key. In search failure rates, we theoretically compare the FBF with hash-based data structures, such as multi-hash table, cuckoo hash table, and d-left hash table. We also provide simulation results to prove the validity of our theoretical results. The simulation results show that the search failure rates of hash tables are larger than that of the functional Bloom filter when the load factor is larger than 0.6.

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“…The role of the PDBF is to store all the challenge information and challenge length held on the authentication server to improve the security of the PWM-PUF. The PDBF is the structure referred to in [49], [53], and [54] in which one cell stores 2 bits, and the false negative of the CBF is removed. Also, the cell positions are shifted from the hash indices by the challenge length.…”

Section: Puf Database Bloom Filter (Pdbf)mentioning

confidence: 99%

“…Figure 8 can be compared with the false positive of theoretical analysis. The theoretical analysis is referenced to in [54]. If n c is the number of challenges in the authentication server, n crp is the number of CRPs in the authentication server, k is the number of hash functions, and m is the number of cells, and p pd is the probability that a particular cell is set at least once by n c elements in the PDBF, p pd is defined in Equation 3:…”

Section: Performance Evaluationmentioning

confidence: 99%

A Novel Hardware Security Architecture: PD-CRP(PUF Database & Challenge-Response Pair) Bloom Filter on Memristor Based PUF

Lee¹,

Choi²,

Kim³

et al. 2020

Preprint

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Because the development of the internet of things (IoT) requires technology that transfers information between objects without human intervention, the core of IoT security will be secure authentication between devices or between devices and servers. Software-based authentication may be a security vulnerability in IoT, but hardware-based security technology can provide a strong security environment. A physical unclonable functions (PUFs) are a hardware security element suitable for lightweight applications. PUFs can generate challenge-response pairs(CRPs) that cannot be controlled or predicted by utilizing inherent physical variations that occur in the manufacturing process. In particular, pulse width memristive PUF (PWM-PUF) improves security performance by applying different write pulse widths and bank structures. Bloom filter (BF) is probabilistic data structures that answer membership queries using small memories. Bloom filter can improve search performance and reduce memory usage and are used in areas such as networking, security, big data, and IoT. In this paper, we propose a structure that applies Bloom filters based on the PWM-PUF to reduce PUF data transmission errors. The proposed structure uses two different Bloom filter types that store different information and that are located in front of and behind the PWM-PUF, improving security by removing challenges from attacker access. Simulation results show that the proposed structure decreases the data transmission error rate and reuse rate as the Bloom filter size increases, the simulation results also show that the proposed structure improves PWM-PUF security with a very small Bloom filter memory.

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Dual-load Bloom filter: Application for name lookup

Cited by 15 publications

References 28 publications

Route Prefix Caching Using Bloom Filters in Named Data Networking

Route Prefix Caching Using Bloom Filters in Named Data Networking

Comparison on Search Failure between Hash Tables and a Functional Bloom Filter

A Novel Hardware Security Architecture: PD-CRP(PUF Database & Challenge-Response Pair) Bloom Filter on Memristor Based PUF

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