Enabling fine-grained multi-keyword search supporting classified sub-dictionaries over encrypted cloud data

Using cloud computing, individuals can store their data on remote servers and allow data access to public users through the cloud servers. As the outsourced data are likely to contain sensitive privacy information, they are typically encrypted before uploaded to the cloud. This, however, significantly limits the usability of outsourced data due to the difficulty of searching over the encrypted data. In this paper, we address this issue by developing the fine-grained multi-keyword search schemes over encrypted cloud data. Our original contributions are three-fold. First, we introduce the relevance scores and preference factors upon keywords which enable the precise keyword search and personalized user experience. Second, we develop a practical and very efficient multi-keyword search scheme. The proposed scheme can support complicated logic search the mixed “AND”, “OR” and “NO” operations of keywords. Third, we further employ the classified sub-dictionaries technique to achieve better efficiency on index building, trapdoor generating and query. Lastly, we analyze the security of the proposed schemes in terms of confidentiality of documents, privacy protection of index and trapdoor, and unlinkability of trapdoor. Through extensive experiments using the real-world dataset, we validate the performance of the proposed schemes. Both the security analysis and experimental results demonstrate that the proposed schemes can achieve the same security level comparing to the existing ones and better performance in terms of functionality, query complexity and efficiency.

Joint learning and dictionary construction for pattern recognition

We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier parameters. Formulating an optimization problem that combines the objective function of the classification with the representation error of both labeled and unlabeled data, constrained by sparsity, we propose an algorithm that alternates between solving for subsets of parameters, whilst preserving the sparsity. The method is then evaluated over two important classification problems in computer vision: object categorization of natural images using the Caltech 101 database and face recognition using the Extended Yale B face database. The results show that the proposed method is competitive against other recently proposed sparse overcomplete counterparts and considerably outperforms many recently proposed face recognition techniques when the number training samples is small.

Sparse Representations of Image via Overcomplete Dictionary Learned by Adaptive Non-orthogonal Sparsifying Transform

How to learn an over complete dictionary for sparse representations of image is an important topic in machine learning, sparse coding, blind source separation, etc. The so-called K-singular value decomposition (K-SVD) method [3] is powerful for this purpose, however, it is too time-consuming to apply. Recently, an adaptive orthogonal sparsifying transform (AOST) method has been developed to learn the dictionary that is faster. However, the corresponding coefficient matrix may not be as sparse as that of K-SVD. For solving this problem, in this paper, a non-orthogonal iterative match method is proposed to learn the dictionary. By using the approach of sequentially extracting columns of the stacked image blocks, the non-orthogonal atoms of the dictionary are learned adaptively, and the resultant coefficient matrix is sparser. Experiment results show that the proposed method can yield effective dictionaries and the resulting image representation is sparser than AOST.

'Iconic development' : is there such a thing?

By way of definition, dictionaries such as the Oxford Dictionary note that the word ‘icon’ is a noun meaning ‘a person or thing regarded as a representative symbol or as worthy of veneration’ (p. 654). More recently, the phrase ‘iconic development’ has crept into the town planning vernacular making reference to built or planned works that are intended to be an urban ‘benchmark’. However, in terms of planning practice in the State of Victoria, Australia, the meaning of ‘iconic development’ is unclear amongst property development professionals – one only needs to peruse the local newspaper, The Age on any given week and publications by key bodies such as the Planning Institute of Australia, the Victorian Planning and Environment Law Association, the Urban Development Institute of Australia and the Property Council of Australia on their websites and in professional newsletters, to appreciate the different ways the phrase is used in the property development context. This paper reports on key themes arising from interviews with 52 Victorian property development professionals, who were interviewed to determine their perceptions about what constitutes ‘iconic development’ in Victoria. Arising from the interviews, the study confirms that ‘iconic development’ is the new buzz phrase in property development circles, it is no longer just being used as a sales and marketing tool, but has been appropriated by technical professionals and introduced into different planning documents including certain local clauses in the Victorian Planning Provisions. Different professionals in the development industry, such as architects, planners and lawyers ascribe different meanings to this phrase, thereby presenting confusion in the property development industry. By understanding what these different professionals mean by the phrase ‘iconic development’ a more collaborative understanding of the phrase is arrived at to provide the practitioner and public with a more informed understanding to review realised and/or planned 'iconic development’.