Inverted Index Table Research Articles

A secure and efficient log storage and query framework based on blockchain

Log data is crucial for security threat detection and audit analysis. However, traditional log systems are susceptible to tampering, posing a significant security risk to information systems. Although blockchain technology has been introduced to enhance tamper resistance, existing blockchain-based log systems still suffer from storage and query efficiency issues. In this paper, we propose a novel secure and efficient log storage and query framework that combines on-chain and off-chain collaboration. An inverted index table is constructed by extracting keywords from logs, which are stored on the blockchain as on-chain data, while the logs themselves are maintained as off-chain data. This approach facilitates the rapid retrieval of specific keywords and ensures the immutability of the logs. Furthermore, we propose a secure and efficient log query method featuring a smart contract designed to automatically handle requests from legitimate log queriers. We also design a data structure based on merkle adaptive radix tree (MART) and merkle B+ tree (MBT) to store index entries, thereby achieving efficient log retrieval. We provide formal security proofs and comprehensively evaluate the proposed framework’s performance experimentally. Results demonstrate that MBT and MART reduce average query times by 20.09% and 51% respectively, compared to the state-of-the-art schemes.

Multi-Keyword Ranked Searchable Encryption with the Wildcard Keyword for Data Sharing in Cloud Computing

Abstract Multi-keyword ranked searchable encryption (MRSE) supports multi-keyword contained in one query and returns the top-k search results related to the query keyword set. It realized effective search on encrypted data. Most previous works about MRSE can only make the complete keyword search and rank on the server-side. However, with more practice, users may not be able to express some keywords completely when searching. Server-side ranking increases the possibilities of the server inferring some keywords queried, leading to the leakage of the user’s sensitive information. In this paper, we propose a new MRSE system named ‘multi-keyword ranked searchable encryption with the wildcard keyword (MRSW)’. It allows the query keyword set to contain a wildcard keyword by using Bloom filter (BF). Using hierarchical clustering algorithm, a clustering Bloom filter tree (CBF-Tree) is constructed, which improves the efficiency of wildcard search. By constructing a modified inverted index (MII) table on the basis of the term frequency-inverse document frequency (TF-IDF) rule, the ranking function of MRSW is performed by the user. MRSW is proved secure under adaptive chosen-keyword attack (CKA2) model, and experiments on a real data set from the web of science indicate that MRSW is efficient and practical.

Effective and efficient indexing in cross-modal hashing-based datasets

To overcome the barrier of storage and computation, the hashing technique has been widely used for nearest neighbor search in multimedia retrieval applications recently. Particularly, cross-modal retrieval that searches across different modalities becomes an active but challenging problem. Although numerous of cross-modal hashing algorithms are proposed to yield compact binary codes, exhaustive search is impractical for large-scale datasets, and Hamming distance computation suffers inaccurate results. In this paper, we propose a novel search method that utilizes a probability-based index scheme over binary hash codes in cross-modal retrieval. The proposed indexing scheme employs a few binary bits from the hash code as the index code. We construct an inverted index table based on the index codes, and train a neural network for ranking and indexing to improve the retrieval accuracy. Experiments are performed on two benchmark datasets for retrieval across image and text modalities, where hash codes are generated and compared with several state-of-the-art cross-modal hashing methods. Results show the proposed method effectively boosts the performance on search accuracy, computation cost, and memory consumption in these datasets and hashing methods. The source code is available on https://github.com/msarawut/HCI.

A New Secure and Lightweight Searchable Encryption Scheme over Encrypted Cloud Data

Searchable Encryption is an emerging cryptographic technique that enables searching capabilities over encrypted data on the cloud. In this paper, a novel searchable encryption scheme for the client-server architecture has been presented. The scheme exploits the properties of the modular inverse to generate a probabilistic trapdoor which facilitates the search over the secure inverted index table. We propose indistinguishability that is achieved by using the property of a probabilistic trapdoor. We design and implement a proof of concept prototype and test our scheme with a real dataset of files. We analyze the performance of our scheme against our claim of the scheme being light weight. The security analysis yields that our scheme assures a higher level of security as compared to other existing schemes.

Using GSW FHE to provide stronger privacy protection for searchable encryption

Nowadays, with increasingly strong awareness of privacy protection, more and more people choose to encrypt their data and then outsource it to the server, but how to search the encrypted data is a challenging problem in this situation. Many people have studied this problem, not only for the fundamental property of this, but also because there is a practical issue where the volume of data is so huge that it is impractical for users to download the whole data and search on it. One common solution is to build an inverted index using a special algorithm and support searchable algorithm on the encrypted data, but this causes another problem: the privacy will be leaked to the data owner when the owner and user are not the same person. In this paper, we design a novel scheme to solve this problem. We provide stronger privacy protection for users when they search on the data, and the scheme can protect the privacy of the inverted index table and content of data. Besides, the data owner cannot get any information about the query messages searched by users. Also, we analyse the security of our scheme, and the simulation demonstrates the good performance of our scheme.

Kernelized Neighborhood Preserving Hashing for Social-Network-Oriented Digital Fingerprints

Digital fingerprinting is a promising approach to protect multimedia content from unauthorized redistribution. However, the existing fingerprints are unsuitable for social network tasks, because they fail to represent the social network structure, which incurs inefficient fingerprint coding. In addition, they are infeasible to efficiently trace colluders due to the large scale of social networks. To address these problems, we design a novel fingerprint, which consists of community relationship code and user identification code. Aiming to preserving the social network structure, we propose a kernelized neighborhood preserving hashing method to generate community relationship codes. The proposed method assigns similar community relationship codes to users in the same or close communities, which improves the anticollusion performance. Because the community relationship codes are binary and neighborhood preserving, they can be used for fast indexing and retrieval. To accelerate the collusion fingerprint tracing, we treat the community relationship codes as index keys to construct a hash table and an inverted index table. Based on the tables, we correspondingly propose an efficient fingerprint detection method. Extensive experiments show that the proposed fingerprint is suitable for social network tasks and the real colluders can be efficiently identified by the proposed fingerprint detection approach.

Fast multi-class action recognition by querying inverted index tables

A fast inverted index based algorithm is proposed for multi-class action recognition. This approach represents an action as a sequence of action states. Here, the action states are cluster centers of the extracted shape-motion features. At first, we compute the shape-motion features of a tracked actor. Secondly, a state binary tree is built by hierarchically clustering the extracted features. Then the training videos are represented as sequences of action states by searching the state binary tree. Based on the labeled state sequences, we create a state inverted index table and a state transition inverted index table. During testing, after representing a new action video as a state sequence, the state and state transition scores are computed by querying the inverted index tables. With the weight trained by the validation set, we get an action class score vector. The recognized action class label is the index of the maximum component of the score vector. Our key contribution is that we propose a fast multi-class action recognition approach based on two inverted index tables. Experiments on several challenging data sets confirm the performance of this approach.

The Key as Dictionary Compression Method of Inverted Index Table under the Hbase Database

Starting with Hbase's own characteristics, this paper designs an inverted index table which includes key word, document ID and position list, and the table can saves a lot of storage space. After then, on the basis of the table, the paper provides key as dictionary compression with high compression ratio and high decompression rate for the data block. At last, this paper tests the effectiveness of the compression method by comparing it with Lzo and Gzip which supported by Hbase.