Adaptive Compression Trie Based Bloom Filter: Request Filter for NDN Content Store

Zhang, Ran; Liu, Jiang; Huang, Tao; Pan, Tian; Lixuan, Wu

doi:10.1109/access.2017.2764106

Cited by 13 publications

(5 citation statements)

References 19 publications

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“…A fetch-fetch BF scheme was designed by Yan to reduce the multiple fetches from the original BF to one [9]. In 2017, Zhang et al [10] proposed the adaptive compression Trie-based Bloom Filter for filtering out unmatched requests in an effort to reduce the average lookup latency of the CS. This solution is mainly composed of a Bloom filter, that is, the Trie and Counting Bloom Filter.…”

Section: Related Workmentioning

confidence: 99%

Search mechanism for data contents based on bloom filter and tree hybrid structure in system wide information management

et al. 2023

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The system wide information management (SWIM) system infrastructure layer uses information‐centric networking (ICN) to implement the cache routing and sharing of air traffic information data. The SWIM network searches and forwards routes on the basis of content names. The variable length, hierarchical name structure, and routing updates caused by the frequent publication and deletion of content make the implementation of fast name routing lookup algorithms a significant but arduous task. To address this problem, this study designed a name matching mechanism on the basis of the hybrid structure of the Bloom filter and the Tree. Compared with the traditional Bloom filter search scheme, this search mechanism reduces the quantity of Bloom filter insertion entries and reduces the possibility of hash collisions. Compared with the traditional Tree search scheme, the proposed mechanism can effectively solve the problem of numerous memory accesses caused by extremely long name prefixes, improving the query efficiency. In addition, the proposed composite structure is compared with several methods, such as DIPIT and NPT, by analyzing the effects of layering, global delay and search speed. The experimental results show that the proposed structure has a favourable performance in terms of storage overhead and search speed.

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Section: Related Workmentioning

confidence: 99%

Search mechanism for data contents based on bloom filter and tree hybrid structure in system wide information management

et al. 2023

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“…A Bloom filter [45] represents the membership information of a set using a simple bit-vector-based data structure. Bloom filters have recently been applied in many applications [46][47][48][49] and proposed in others [50][51][52]. A Bloom filter uses a hash function k on an m bit array that is initialized to 0 to store the elements n of a set into an array.…”

Section: Bloom Filtermentioning

confidence: 99%

A Novel Hardware Security Architecture for IoT Device: PD-CRP (PUF Database and Challenge–Response Pair) Bloom Filter on Memristor-Based PUF

et al. 2020

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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. Physical unclonable functions (PUFs) are 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, the pulsewidth-based memristive PUF (pm-PUF) improves security performance by applying different write pulse widths and bank structures. Bloom filters (BFs) are probabilistic data structures that answer membership queries using small memories. Bloom filters 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 pm-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 pm-PUF, reducing unnecessary access 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 pm-PUF security with a very small Bloom filter memory.

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“…To construct a practical and secure PUF, methods have been proposed to evaluate PUF performance using performance metrics such as randomness, diffuseness, uniqueness, and steadiness which are defined as follows [41] [42] represents the membership information of a set using a simple bit-vector based data structure. Bloom filters have recently been applied in many applications [43]- [46] and proposed in others [47]- [49]. A Bloom filter uses a hash function k on an m bit array that is initialized to 0 to store the elements n of a set into an array.…”

Section: Conventional Puf and Memristor-based Pufmentioning

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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Adaptive Compression Trie Based Bloom Filter: Request Filter for NDN Content Store

Cited by 13 publications

References 19 publications

Search mechanism for data contents based on bloom filter and tree hybrid structure in system wide information management

Search mechanism for data contents based on bloom filter and tree hybrid structure in system wide information management

A Novel Hardware Security Architecture for IoT Device: PD-CRP (PUF Database and Challenge–Response Pair) Bloom Filter on Memristor-Based PUF

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

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