DYNAMIC MULTI-KEYWORD RANKING SCHEME ON ENCRYPTED CLOUD DATA

Due to the increasing popularity of cloud computing, more and more data owners are motivated to outsource their data to cloud servers for great convenience and reduced cost in data management. However, sensitive data should be encrypted before outsourcing for privacy requirements, which obsoletes data utilization like keyword-based document retrieval. In this paper, we present a secure multi-keyword ranked search scheme over encrypted cloud data, which simultaneously supports dynamic update operations like deletion and insertion of documents. Specifically, the vector space model and the widely-used TF_IDF model are combined in the index construction and query generation. We construct a special tree-based index structure and propose a “Greedy Depth-first Search” algorithm to provide efficient multi-keyword ranked search. The secure kNN algorithm is utilized to encrypt the index and query vectors, and meanwhile ensure accurate relevance score calculation between encrypted index and query vectors. In order to resist statistical attacks, phantom terms are added to the index vector for blinding search results. Due to the use of our special tree-based index structure, the proposed scheme can achieve sub-linear search time and deal with the deletion and insertion of documents flexibly. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme

PROTECTED AND DYNAMIC KEYWORD SEARCH RANK SCHEME FOR CLOUD DATABASE

The major aim of this paper is to solve the problem of multi-keyword ranked search over encrypted cloud data (MRSE) at the time of protecting exact method wise privacy in the cloud computing concept. Data holders are encouraged to outsource their difficult data management systems from local sites to the business public cloud for large flexibility and financial savings. However for protecting data privacy, sensitive data have to be encrypted before outsourcing, which performs traditional data utilization based on plaintext keyword search. As a result, allowing an encrypted cloud data search service is of supreme significance. In view of the large number of data users and documents in the cloud, it is essential to permit several keywords in the search demand and return documents in the order of their appropriate to these keywords. Similar mechanism on searchable encryption makes centre on single keyword search or Boolean keyword search, and rarely sort the search results. In the middle of various multi-keyword semantics, deciding the well-organized similarity measure of “coordinate matching,” it means that as many matches as possible, to capture the appropriate data documents to the search query. Particularly, we consider “inner product similarity” i.e., the amount of query keywords shows in a document, to quantitatively estimate such match measure that document to the search query. Through the index construction, every document is connected with a binary vector as a sub index where each bit characterize whether matching keyword is contained in the document. The search query is also illustrates as a binary vector where each bit means whether corresponding keyword appears in this search request, so the matched one could be exactly measured by the inner product of the query vector with the data vector. On the other hand, directly outsourcing the data vector or the query vector will break the index privacy or the search privacy. The vector space model facilitate to offer enough search accuracy, and the DES encryption allow users to occupy in the ranking while the popularity of computing work is done on the server side by process only on cipher text. As a consequence, data leakage can be eradicated and data security is guaranteed

ANONYMOUS ROUTING FOR PRIVACY-PRESERVING DISTRIBUTED COMPUTING

Master'sMASTER OF SCIENC

Robust Mobile Visual Recognition System: From Bag of Visual Words to Deep Learning

With billions of images captured by mobile users everyday, automatically recognizing contents in such images has become a particularly important feature for various mobile apps, including augmented reality, product search, visual-based authentication etc. Traditionally, a client-server architecture is adopted such that the mobile client sends captured images/video frames to a cloud server, which runs a set of task-specific computer vision algorithms and sends back the recognition results. However, such scheme may cause problems related to user privacy, network stability/availability and device energy.In this dissertation, we investigate the problem of building a robust mobile visual recognition system that achieves high accuracy, low latency, low energy cost and privacy protection. Generally, we study two broad types of recognition methods: the bag of visual words (BOVW) based retrieval methods, which search the nearest neighbor image to a query image, and the state-of-the-art deep learning based methods, which recognize a given image using a trained deep neural network. The challenges of deploying BOVW based retrieval methods include: size of indexed image database, query latency, feature extraction efficiency and re-ranking performance. To address such challenges, we first proposed EMOD which enables efficient on-device image retrieval on a downloaded context-dependent partial image database. The efficiency is achieved by analyzing the BOVW processing pipeline and optimizing each module with algorithmic improvement.Recent deep learning based recognition approaches have been shown to greatly exceed the performance of traditional approaches. We identify several challenges of applying deep learning based recognition methods on mobile scenarios, namely energy efficiency and privacy protection for real-time visual processing, and mobile visual domain biases. Thus, we proposed two techniques to address them, (i) efficiently splitting the workload across heterogeneous computing resources, i.e., mobile devices and the cloud using our Moca framework, and (ii) using mobile visual domain adaptation as proposed in our collaborative edge-mediated platform DeepCham. Our extensive experiments on large-scale benchmark datasets and off-the-shelf mobile devices show our solutions provide better results than the state-of-the-art solutions

Computer science: the hardware software and heart of IT

1st edition, 201

Geographic information extraction from texts

A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction

Strategies for Unbridled Data Dissemination: An Emergency Operations Manual

This project is a study of free data dissemination and impediments to it. Drawing upon post-structuralism, Actor Network Theory, Participatory Action Research, and theories of the political stakes of the posthuman by way of Stirnerian egoism and illegalism, the project uses a number of theoretical, technical and legal texts to develop a hacker methodology that emphasizes close analysis and disassembly of existent systems of content control. Specifically, two tiers of content control mechanisms are examined: a legal tier, as exemplified by Intellectual Property Rights in the form of copyright and copyleft licenses, and a technical tier in the form of audio, video and text-based watermarking technologies. A series of demonstrative case studies are conducted to further highlight various means of content distribution restriction. A close reading of a copyright notice is performed in order to examine its internal contradictions. Examples of watermarking employed by academic e-book and journal publishers and film distributors are also examined and counter-forensic techniques for removing such watermarks are developed. The project finds that both legal and technical mechanisms for restricting the flow of content can be countervailed, which in turn leads to the development of different control mechanisms and in turn engenders another wave of evasion procedures. The undertaken methodological approach thus leads to the discovery of on-going mutation and adaptation of in-between states of resistance. Finally, an analysis of various existent filesharing applications is performed, and a new Tor-based BitTorrent tracker is set up to strengthen the anonymization of established filesharing methods. It is found that there exist potential de-anonymization attacks against all analyzed file-sharing tools, with potentially more secure filesharing options also seeing less user adoption