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Improving False Positive Rate of Extended Learned Bloom Filters Using Grid Search
http://doi.org/10.5626/JOK.2022.49.1.78
Bloom filter is a data structure that represents a set and returns whether data is included or not. However, there are cases in which false positives are returned at the cost of using less space. The learned bloom filter is a variation of the bloom filter, that uses a machine learning model in the pre-processing process to improve the false-positive rate. The learned bloom filter stores some data in the machine learning model, and the leftover data is stored in the auxiliary filter. An auxiliary filter can be implemented by using a bloom filter only, but in this paper, we use the bloom filter and the learned hash function, and this is called an extended learned bloom filter. The learned hash function uses the output value of the machine learning model as a hash function. In this paper, we propose a method that improves the false positive rate of the extended learned bloom filter through grid search. This method explores the extended learned bloom filter with the lowest false positive rate, by increasing the hyperparameter that represents the ratio of the learned hash function. As a result, we experimentally show that the extended learned bloom filter selected through grid search, can have a 20% improvement in false-positive rate compared to the learned bloom filter, in the experiment that needs more than 100,000 data to store. In addition, we also show that the false negative error may occur in the learned hash function by the use of 32-bit floating points in the neural network model. This can be solved by changing the floating points to 64-bit. Finally, we show that in an experiment where we query 10,000 data, we can adjust the structure of the neural network model to save 20KB of space and create an extended learned bloom filter with the same false-positive rate. However, the query time is increased by 2% at the cost of saving 20KB of space.
Xpass: NUMA-aware Persistent Memory Disaggregation
Jaeyoun Nam, Hokeun Cha, ByeongKeon Lee, Beomseok Nam
http://doi.org/10.5626/JOK.2021.48.7.735
The disaggregation method is used for efficient resource management in large-scale data centers, where each server consists of NUMA nodes. In the NUMA architecture, the latency difference between the remote and local access is known to be significant. In particular, remote NUMA access to persistent memory is even higher than DRAM. In this study, we propose Xpass, a memory disaggregation framework that considers the locality of NUMA architecture in a persistent memory disaggregation system. Xpass uses the dynamic hash table - CCEH to manage cached pages, and proposes a segment split algorithm that considers load balancing between the NUMA nodes in a NUMA environment.
An Efficient Large Graph Clustering Technique based on Min-Hash
Graph clustering is widely used to analyze a graph and identify the properties of a graph by generating clusters consisting of similar vertices. Recently, large graph data is generated in diverse applications such as Social Network Services (SNS), the World Wide Web (WWW), and telephone networks. Therefore, the importance of graph clustering algorithms that process large graph data efficiently becomes increased. In this paper, we propose an effective clustering algorithm which generates clusters for large graph data efficiently. Our proposed algorithm effectively estimates similarities between clusters in graph data using Min-Hash and constructs clusters according to the computed similarities. In our experiment with real-world data sets, we demonstrate the efficiency of our proposed algorithm by comparing with existing algorithms.
Data Block based User Authentication for Outsourced Data
Changhee Hahn, Hyunsoo Kown, Daeyeong Kim, Junbeom Hur
Recently, there has been an explosive increase in the volume of multimedia data that is available as a result of the development of multimedia technologies. More and more data is becoming available on a variety of web sites, and it has become increasingly cost prohibitive to have a single data server store and process multimedia files locally. Therefore, many service providers have been likely to outsource data to cloud storage to reduce costs. Such behavior raises one serious concern: how can data users be authenticated in a secure and efficient way? The most widely used password-based authentication methods suffer from numerous disadvantages in terms of security. Multi-factor authentication protocols based on a variety of communication channels, such as SMS, biometric, or hardware tokens, may improve security but inevitably reduce usability. To this end, we present a data block-based authentication scheme that is secure and guarantees usability in such a manner where users do nothing more than enter a password. In addition, the proposed scheme can be effectively used to revoke user rights. To the best of our knowledge, our scheme is the first data block-based authentication scheme for outsourced data that is proven to be secure without degradation in usability. An experiment was conducted using the Amazon EC2 cloud service, and the results show that the proposed scheme guarantees a nearly constant time for user authentication.
Study on the Improvement about User Authentication of Android Third Party Application Through the Vulnerability in Google Voice
Seyeong Lee, Jaekyun Park, Sungdae Hong, Hyoungki Choi
In the Android market, a large portion of the market share consists of third party applications, but not much research has been performed in this respect. Of these applications, mobile Voice Over IP (VoIP) applications are one of the types of applications that are used the most. In this paper, we focus on user authentication methods for three representative applications of the Google Voice service, which is a famous mobile VoIP application. Then, with respect to the Android file system, we developed a method to store and to send user information for authentication. Finally, we demonstrate a vulnerability in the mechanism and propose an improved mechanism for user authentication by using hash chaining and an elliptic curve Diffie-Hellman key exchange.
A Car Black Box Video Data Integrity Assurance Scheme Using Cyclic Data Block Chaining
Kang Yi, Kyung-Mi Kim, Yong Jun Cho
The integrity assurance of recorded video by car black boxes are necessary as the car black box is becoming more popular. In this paper, we propose a video data integrity assurance scheme reflecting the features of car black box. The proposed method can detect any kind of deletion, insertion, modification of frames by cyclic chaining using inter block relation. And, it provides the integrity assurance function consistently even in cases of file overwriting because of no more free space in storage, partial file data lost. And non-repudiation is supported. Experimental results with a car black box embedded system with A8 application processor show that our method has a feasible computational overhead to process full HD resoultion video at 30 frames per second in a real time.
An Implementation of an SHA-3 Hash Function Validation Program and Hash Algorithm on 16bit-UICC
Hee-Woong Lee, Dowon Hong, Hyun-il Kim, ChangHo Seo, Kishik Park
A hash function is an essential cryptographic algorithm primitive that is used to provide integrity to many applications such as message authentication codes and digital signatures. In this paper, we introduce a concept and test method for a Cryptographic Algorithm Validation Program (CAVP). Also, we design an SHA-3 CAVP program and implement an SHA-3 algorithm in 16bit-UICC. Finally, we compare the efficiency of SHA-3 with SHA-2 and evaluate the exellence of the SHA-3 algorithm.
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