Efficient multi-keyword ranked query over encrypted data in cloud computing

Cloud computing infrastructure is a promising new technology and greatly accelerates the development of large scale data storage, processing and distribution. However, security and privacy become major concerns when data owners outsource their private data onto public cloud servers that are not within their trusted management domains. To avoid information leakage, sensitive data have to be encrypted before uploading onto the cloud servers, which makes it a big challenge to support efficient keyword-based queries and rank the matching results on the encrypted data. Most current works only consider single keyword queries without appropriate ranking schemes. In the current multi-keyword ranked search approach, the keyword dictionary is static and cannot be extended easily when the number of keywords increases. Furthermore, it does not take the user behavior and keyword access frequency into account. For the query matching result which contains a large number of documents, the out-of-order ranking problem may occur. This makes it hard for the data consumer to find the subset that is most likely satisfying its requirements. In this paper, we propose a flexible multi-keyword query scheme, called MKQE to address the aforementioned drawbacks. MKQE greatly reduces the maintenance overhead during the keyword dictionary expansion. It takes keyword weights and user access history into consideration when generating the query result. Therefore, the documents that have higher access frequencies and that match closer to the users’ access history get higher rankings in the matching result set. Our experiments show that MKQE presents superior performance over the current solutions.     

面向属性加密数据的可验证细粒度关键字密文搜索

为实现加密数据的细粒度密文搜索,并确保第三方服务器诚实可靠地执行搜索过程,同时尽可能降低用户端的计算和通信代价,提出支持密文搜索可验证的属性基可搜索加密方案。通过引入对称密钥加密体制,承诺方案和强一次性消息认证码,以经典的属性加密方案为基础构造算法,实现密文关键字的细粒度搜索以及搜索过程的可验证性,并证明方案具有选择性的数据安全性和搜索索引安全性,以及验证可靠性。与同类方案相比,该方案在达到同等安全性要求的情况下,进一步提高了终端用户的计算和通信效率。     

加密云数据下的关键词模糊搜索方案

对于加密云数据的搜索,传统的关键词模糊搜索方案虽然能搜索到相关文档,但是搜索的结果并不令人满意。在用户输入正确的情况下,无法完成近似搜索,当用户出现拼写错误时,返回的结果中包含大量无关关键词文档,严重浪费了带宽资源。针对目前在加密云数据下关键词模糊搜索的缺陷,提出了一种新型的关键词模糊搜索方案,通过对关键词计算相关度分数并对文档根据相关度分数进行排序,将top-k（即相关度最高的k个文档）个文档返回给搜索用户,减少了不必要的带宽浪费和用户寻找有效文档的时间消耗,提供了更加有效的搜索结果,并且通过引入虚假陷门集,增大了云服务器对文档关键词的分析难度,增加了系统的隐私性保护。     

支持语义扩展的动态多关键词密文排序检索

针对云存储环境下已有的动态多关键词密文排序检索方案不支持关键词语义扩展、不具备前向安全和后向安全的问题，提出一种支持语义检索且具备前向安全和后向安全的动态多关键词密文排序检索方案。该方案通过构建语义关系图实现查询关键词的语义扩展；使用树索引结构实现数据的检索和动态更新；利用向量空间模型实现多关键词排序搜索；基于安全K近邻算法对维度扩展后的索引和查询向量进行加密。安全性分析表明，该方案在已知密文模型下是安全的且具有动态更新时的前向安全和后向安全。效率分析及仿真实验结果表明，该方案在服务器检索效率方面优于目前同类型具有相同安全性或相同功能的方案。     

基于区块链的公平可验证的多关键词密文排序检索

针对区块链环境下已有的可搜索加密方案实现结果验证和公平支付的成本过高、检索功能局限的问题,提出基于区块链的支持验证与公平支付的多关键词排序检索方案。该方案通过云服务器（CSP）存储加密索引树和执行搜索操作,并且构建了包含验证证明的查找表来辅助智能合约完成检索结果的验证以及公平支付,从而降低智能合约执行操作的复杂性,节约时间和费用成本。此外,结合向量空间模型与词频逆文档频率（TF-IDF）技术构建平衡二叉树结构的索引,并使用安全K邻近对索引和查询向量进行加密,从而实现支持动态更新的多关键词排序检索。安全性和性能分析表明,所提方案在区块链环境下和已知密文模型下是安全可行的;仿真实验结果表明,所提方案能够以可接受的开销实现结果验证与公平支付。     

Semantic and secure search over encrypted outsourcing cloud based on BERT

Searchable encryption provides an effective way for data security and privacy in cloud storage. Users can retrieve encrypted data in the cloud under the premise of protecting their own data security and privacy. However, most of the current content-based retrieval schemes do not contain enough semantic information of the article and cannot fully reflect the semantic information of the text. In this paper, we propose two secure and semantic retrieval schemes based on BERT (bidirectional encoder representations from transformers) named SSRB-1, SSRB-2. By training the documents with BERT, the keyword vector is generated to contain more semantic information of the documents, which improves the accuracy of retrieval and makes the retrieval result more consistent with the user’s intention. Finally, through testing on real data sets, it is shown that both of our solutions are feasible and effective.     

An effective 3-in-1 keyword search method over heterogeneous data sources

Conventional keyword search engines are restricted to a given data model and cannot easily adapt to unstructured, semi-structured or structured data. In this paper, we propose an efficient and adaptive keyword search method, called EASE, for indexing and querying large collections of heterogeneous data. To achieve high efficiency in processing keyword queries, we first model unstructured, semi-structured and structured data as graphs, and then summarize the graphs and construct graph indices instead of using traditional inverted indices. We propose an extended inverted index to facilitate keyword-based search, and present a novel ranking mechanism for enhancing search effectiveness. We have conducted an extensive experimental study using real datasets, and the results show that EASE achieves both high search efficiency and high accuracy, and outperforms the existing approaches significantly.     

Lightweight integration of IR and DB for scalable hybrid search with integrated ranking support

The Web contains a large amount of documents and an increasing quantity of structured data in the form of RDF triples. Many of these triples are annotations associated with documents. While structured queries constitute the principal means to retrieve structured data, keyword queries are typically used for document retrieval. Clearly, a form of hybrid search that seamlessly integrates these formalisms to query both textual and structured data can address more complex information needs. However, hybrid search on the large scale Web environment faces several challenges. First, there is a need for repositories that can store and index a large amount of semantic data as well as textual data in documents, and manage them in an integrated way. Second, methods for hybrid query answering are needed to exploit the data from such an integrated repository. These methods should be fast and scalable, and in particular, they shall support flexible ranking schemes to return not all but only the most relevant results. In this paper, we present CE², an integrated solution that leverages mature information retrieval and database technologies to support large scale hybrid search. For scalable and integrated management of data, CE² integrates off-the-shelf database solutions with inverted indexes. Efficient hybrid query processing is supported through novel data structures and algorithms which allow advanced ranking schemes to be tightly integrated. Furthermore, a concrete ranking scheme is proposed to take features from both textual and structured data into account. Experiments conducted on DBpedia and Wikipedia show that CE² can provide good performance in terms of both effectiveness and efficiency.     

从“人肉搜索”看隐私权与言论自由的冲突与协调

本文从人肉搜索这一现象入手,阐释网络言论自由的现状与特点,通过人肉搜索第一案解析公民隐私权面对人肉搜索时的危机,在最后试图给出平衡网络言论自由与公民隐私权的建议。     

Privacy preserving Internet of Things: From privacy techniques to a blueprint architecture and efficient implementation

The Internet of Things (IoT) is the latest web evolution that incorporates billions of devices that are owned by different organisations and people who are deploying and using them for their own purposes. IoT-enabled harnessing of the information that is provided by federations of such IoT devices (which are often referred to as IoT things) provides unprecedented opportunities to solve internet-scale problems that have been too big and too difficult to tackle before. Just like other web-based information systems, IoT must also deal with the plethora of Cyber Security and privacy threats that currently disrupt organisations and can potentially hold the data of entire industries and even countries for ransom. To realise its full potential, IoT must deal effectively with such threats and ensure the security and privacy of the information collected and distilled from IoT devices. However, IoT presents several unique challenges that make the application of existing security and privacy techniques difficult. This is because IoT solutions encompass a variety of security and privacy solutions for protecting such IoT data on the move and in store at the device layer, the IoT infrastructure/platform layer, and the IoT application layer. Therefore, ensuring end-to-end privacy across these three IoT layers is a grand challenge in IoT. In this paper, we tackle the IoT privacy preservation problem. In particular, we propose innovative techniques for privacy preservation of IoT data, introduce a privacy preserving IoT Architecture, and also describe the implementation of an efficient proof of concept system that utilises all these to ensure that IoT data remains private. The proposed privacy preservation techniques utilise multiple IoT cloud data stores to protect the privacy of data collected from IoT. The proposed privacy preserving IoT Architecture and proof of concept implementation are based on extensions of OpenIoT - a widely used open source platform for IoT application development. Experimental evaluations are also provided to validate the efficiency and performance outcomes of the proposed privacy preserving techniques and architecture.     

Standard versus uniform binary search and their variants in learned static indexing: The case of the searching on sorted data benchmarking software platform

Learned Indexes use a model to restrict the search of a sorted table to a smaller interval. Typically, a final binary search is done using the lower_bound routine of the Standard C++ library. Recent studies have shown that on current processors other search approaches (such as k-ary search) can be more efficient in some applications. Using the SOSD learned indexing benchmarking software, we extend these results to show that k-ary search is indeed a better choice when using learned indexes. We highlight how such a choice may be dependent on the computer architecture used, for example, Intel I7 or Apple M1, and provide guidelines for the selection of the Search routine within the learned indexing framework.     

基于谱分析的密度峰值快速聚类算法

针对密度峰值快速聚类（CFSFDP）算法对不同数据集聚类效果的差异，利用谱聚类对密度峰值快速聚类算法加以改进，提出了一种基于谱分析的密度峰值快速聚类算法CFSFDP-SA。首先，将高维非线性的数据集映射到低维子空间上实现降维处理，将聚类问题转化为图的最优划分问题以增强算法对数据全局结构的适应性；然后，利用CFSFDP算法对处理后的数据集进行聚类。结合这两种聚类算法各自的优势，能进一步提升聚类算法的性能。在5个人工合成数据集（2个线性数据集和3个非线性数据集）与4个UCI数据库中真实数据集上的聚类结果显示，相比CFSFDP算法，CFSFDP-SA算法的聚类精度有一定提升，在高维数据集的聚类精度上最多提高了14%，对原始数据集的适应性更强。     

面向领域的语义搜索引擎的应用研究

针对基于关键字的搜索引擎缺乏语义的问题,提出了一种面向专业领域的语义搜索引擎模型.以领域本体形式化描述为基础,构建本体语义框架,进而给出语义搜索模型.在模型中,以概念、概念-实例以及关键字等3种扩展特征项作为基础,对查询扩展算法和文档语义标注算法进行了研究,并且构建了语义索引,通过引入向量空间模型判定扩展检索词与语义文档的相似度.实验结果表明,该模型较传统模型较大提高了检索的查准率和查全率.     

具有捕食策略的混合随机优化算法及其多极值函数优化

SAGACIA是一种混合随机优化算法,该算法虽已吸收了模拟退火算法、遗传算法和趋化性算法的优点,但搜索过程中仍存在收敛速度慢以及采用固定步长影响搜索精度的缺点,而捕食搜索策略通过限制的调节能较快锁定最优区域,从而提高收敛速度。结合两者的优缺点,提出一种具有捕食搜索策略的自适应调整步长SAGACIA算法,改进后的算法通过捕食搜索策略平衡了算法的局域搜索和全局搜索,提高了收敛速度;邻域搜索采用自适应步长,避免了最优解附近的震荡,提高了搜索精度。实验仿真结果表明,改进后的SAGACIA算法具有较快的收敛速度和较高的寻优精度,证明了算法改进的有效性和可行性。     

Distinguishability of interval type-2 fuzzy sets data by analyzing upper and lower membership functions

In this paper, we deal with the problem of classification of interval type-2 fuzzy sets through evaluating their distinguishability. To this end, we exploit a general matching algorithm to compute their similarity measure. The algorithm is based on the aggregation of two core similarity measures applied independently on the upper and lower membership functions of the given pair of interval type-2 fuzzy sets that are to be compared. Based on the proposed matching procedure, we develop an experimental methodology for evaluating the distinguishability of collections of interval type-2 fuzzy sets. Experimental results on evaluating the proposed methodology are carried out in the context of classification by considering interval type-2 fuzzy sets as patterns of suitable classification problem instances. We show that considering only the upper and lower membership functions of interval type-2 fuzzy sets is sufficient to (i) accurately discriminate between them and (ii) judge and quantify their distinguishability.     

Theory of Genetic Algorithms II: models for genetic operators over the string-tensor representation of populations and convergence to global optima for arbitrary fitness function under scaling

We present a theoretical framework for an asymptotically converging, scaled genetic algorithm which uses an arbitrary-size alphabet and common scaled genetic operators. The alphabet can be interpreted as a set of equidistant real numbers and multiple-spot mutation performs a scalable compromise between pure random search and neighborhood-based change on the alphabet level. We discuss several versions of the crossover operator and their interplay with mutation. In particular, we consider uniform crossover and gene-lottery crossover which does not commute with mutation. The Vose–Liepins version of mutation-crossover is also integrated in our approach. In order to achieve convergence to global optima, the mutation rate and the crossover rate have to be annealed to zero in proper fashion, and unbounded, power-law scaled proportional fitness selection is used with logarithmic growth in the exponent. Our analysis shows that using certain types of crossover operators and large population size allows for particularly slow annealing schedules for the crossover rate. In our discussion, we focus on the following three major aspects based upon contraction properties of the mutation and fitness selection operators: (i) the drive towards uniform populations in a genetic algorithm using standard operations, (ii) weak ergodicity of the inhomogeneous Markov chain describing the probabilistic model for the scaled algorithm, (iii) convergence to globally optimal solutions. In particular, we remove two restrictions imposed in Theorem 8.6 and Remark 8.7 of (Theoret. Comput. Sci. 259 (2001) 1) where a similar type of algorithm is considered as described here: mutation need not commute with crossover and the fitness function (which may come from a coevolutionary single species setting) need not have a single maximum.