JPEG encryption for image rescaling in the encrypted domain

This work proposes a novel protocol of encrypting the JPEG image suitable for image rescaling in the encrypted domain. To protect the privacy of original content, the image owner perturbs the texture and randomizes the structure of the JPEG image by enciphering the quantized Discrete Cosine Transform (DCT) coefficients. After receiving the encrypted JPEG image, the service provider generates a rescaled JPEG image by down-sampling the encrypted DCT coefficients. On the recipient side, the encrypted JPEG image rescaled by the service provider can be decrypted to a plaintext image with a lower resolution with the aid of encryption keys. Experimental results show that the proposed method has a good capability of rescaling the privacy-protected JPEG file.     

Spline regression hashing for fast image search

Techniques for fast image retrieval over large databases have attracted considerable attention due to the rapid growth of web images. One promising way to accelerate image search is to use hashing technologies, which represent images by compact binary codewords. In this way, the similarity between images can be efficiently measured in terms of the Hamming distance between their corresponding binary codes. Although plenty of methods on generating hash codes have been proposed in recent years, there are still two key points that needed to be improved: 1) how to precisely preserve the similarity structure of the original data and 2) how to obtain the hash codes of the previously unseen data. In this paper, we propose our spline regression hashing method, in which both the local and global data similarity structures are exploited. To better capture the local manifold structure, we introduce splines developed in Sobolev space to find the local data mapping function. Furthermore, our framework simultaneously learns the hash codes of the training data and the hash function for the unseen data, which solves the out-of-sample problem. Extensive experiments conducted on real image datasets consisting of over one million images show that our proposed method outperforms the state-of-the-art techniques.     

End-to-end privacy preserving scheme for IoT-based healthcare systems

Preserving patients’ privacy is one of the most important challenges in IoT-based healthcare systems. Although patient privacy has been widely addressed in previous work, there is a lack of a comprehensive end-to-end approach that simultaneously preserves the location and data privacy of patients assuming that system entities are untrusted. Most of the past researches assume that parts of this end-to-end system are trustworthy while privacy may be threatened by insider attacks. In this paper, we propose an end-to-end privacy preserving scheme for the patients assuming that all main entities of the healthcare system (including sensors, gateways, and application providers) are untrusted. The proposed scheme preserves end-to-end privacy against insider threats as well as external attacks concerning the resource restrictions of the sensors. This scheme provides mutual authentication between main entities while preserves patients’ anonymity. Only the allowed users can access the real identity of patients alongside their locations and their healthcare information. Informal security analysis and formal security verification of the proposed protocol in AVISPA show that it is secure against impersonation, replay, modification, and man-in-the-middle attacks. Moreover, performance assessments show that the proposed protocol provides more security services without considerable growth in the computation overhead of the sensors. Also, it is shown that the proposed protocol diminishes the signaling overhead of the sensors and so their energy consumption compared to the literature at the expense of adding a little more signaling overhead to the gateways.

     

ESOT: a new privacy model for preserving location privacy in Internet of Things

The Internet of Things (IoT) means connecting everything with every other thing through the Internet. In IoT, millions of devices communicate to exchange data and information with each other. During communication, security and privacy issues arise which need to be addressed. To protect information about users’ location, an efficient technique should be devised. Several techniques have already been proposed for preserving location privacy in IoT. However, the existing research lags in preserving location privacy in IoT and has highlighted several issues such as being specific or being restricted to a certain location. In this paper, we propose a new location privacy technique called the enhanced semantic obfuscation technique (ESOT) to preserve the location information of a user. Experimental results show that ESOT achieves improved location privacy and service utility when compared with a well-known existing approach, the semantic obfuscation technique.     

Using trusted computing for privacy preserving keystroke-based authentication in smartphones

Smartphones are increasingly being used to store personal information as well as to access sensitive data from the Internet and the cloud. Establishment of the identity of a user requesting information from smartphones is a prerequisite for secure systems in such scenarios. In the past, keystroke-based user identification has been successfully deployed on production-level mobile devices to mitigate the risks associated with naïve username/password based authentication. However, these approaches have two major limitations: they are not applicable to services where authentication occurs outside the domain of the mobile device—such as web-based services; and they often overly tax the limited computational capabilities of mobile devices. In this paper, we propose a protocol for keystroke dynamics analysis which allows web-based applications to make use of remote attestation and delegated keystroke analysis. The end result is an efficient keystroke-based user identification mechanism that strengthens traditional password protected services while mitigating the risks of user profiling by collaborating malicious web services. We present a prototype implementation of our protocol using the popular Android operating system for smartphones.     

网络中多敏感属性数据发布隐私保护研究

随着网络技术的快速发展,许多社会网站被创建和使用,使得关于个人的社会网络信息大量被收集和发布.为了保证个人隐私的安全,本文提出了一个新的集值属性(k,l)anonymity隐私原则,开发了一个满足这个隐私原则的隐私算法来高效地处理集值属性数据流.并通过实验进行了验证本算法的高效率和有效性.     

Secure watermarking scheme against watermark attacks in the encrypted domain

The homomorphic cryptosystems create a great opportunity for secure signal processing (SSP). Watermarking in the encrypted domain provides a promising solution to the security of watermarking. However, the robust performance of the watermarking scheme in the encrypted domain should be carefully considered. In this paper, we propose a robust watermarking scheme in the encrypted domain, which protects the original images from the third party embedders. The hybrid discrete wavelet transform (DWT) and discrete cosine transform (DCT) based method improves the robust performance of the encrypted domain watermarking scheme. The watermark extraction can be performed on both the plaintext and the encrypted domains. We also give an estimation of the expanding factor after watermark embedding in the encrypted domain. At last, we discuss the watermark attacks that are available in the encrypted domain. Taking Haar wavelet transform for example, we conduct the experiments on the visual quality and the robustness of our watermarking scheme, which demonstrate that the entire performance is satisfactory.     

Random Gray code and its performance analysis for image hashing

The discrete-binary conversion stage, which plays the role of converting quantized hash vectors into binary hash strings by encoding, is one of the most important parts of authentication-oriented image hashing. However, very few works have been done on the discrete-binary conversion stage. In this paper, based on Gray code, we propose a key-dependent code called random Gray (RGray) code for image hashing, which, according to our theoretical analysis and experimental results, is likely to increase the security of image hashing to some extent and meanwhile maintains the performance of Gray code in terms of the tradeoff between robustness and fragility. We also apply a measure called distance distortion, which was proposed by Rothlauf (2002) [1] for evolutionary search, to investigate the influence of the discrete-binary conversion stage on the performance of image hashing. Based on distance distortion, we present a theoretical comparison of the encodings applied in the discrete-binary conversion stage of image hashing, including RGray encoding. And our experimental results validate the practical applicability of distance distortion on the performance evaluation of the discrete-binary conversion stage.     

Directional detail histogram for image retrieval

The authors propose a new indexing technique, for image retrieval, which calculates the histogram of the directional detail content in a given image. A multiresolution analysis is applied to extract directional information which is then mapped into three-dimensional vectors and presented as a histogram. This allows the use of histogram query techniques to be readily applied for retrieval     

Semantic clustering for region-based image retrieval

With the proliferation of applications that demand content-based image retrieval, two merits are becoming more desirable. The first is the reduced search space, and the second is the reduced “semantic gap.” This paper proposes a semantic clustering scheme to achieve these two goals. By performing clustering before image retrieval, the search space can be significantly reduced. The proposed method is different from existing image clustering methods as follows: (1) it is region based, meaning that image sub-regions, instead of the whole image, are grouped into. The semantic similarities among image regions are collected over the user query and feedback history; (2) the clustering scheme is dynamic in the sense that it can evolve to include more new semantic categories. Ideally, one cluster approximates one semantic concept or a small set of closely related semantic concepts, based on which the “semantic gap” in the retrieval is reduced.     

Feature selection for content-based image retrieval

In this article, we propose a novel system for feature selection, which is one of the key problems in content-based image indexing and retrieval as well as various other research fields such as pattern classification and genomic data analysis. The proposed system aims at enhancing semantic image retrieval results, decreasing retrieval process complexity, and improving the overall system usability for end-users of multimedia search engines. Three feature selection criteria and a decision method construct the feature selection system. Two novel feature selection criteria based on inner-cluster and intercluster relations are proposed in the article. A majority voting-based method is adapted for efficient selection of features and feature combinations. The performance of the proposed criteria is assessed over a large image database and a number of features, and is compared against competing techniques from the literature. Experiments show that the proposed feature selection system improves semantic performance results in image retrieval systems. This work was supported by the Academy of Finland, Project No. 213,462 (Finnish Centre of Excellence Program 2006–2011).     

Fast wavelet-based image characterization for highly adaptive image retrieval

Adaptive wavelet-based image characterizations have been proposed in previous works for content-based image retrieval (CBIR) applications. In these applications, the same wavelet basis was used to characterize each query image: This wavelet basis was tuned to maximize the retrieval performance in a training data set. We take it one step further in this paper: A different wavelet basis is used to characterize each query image. A regression function, which is tuned to maximize the retrieval performance in the training data set, is used to estimate the best wavelet filter, i.e., in terms of expected retrieval performance, for each query image. A simple image characterization, which is based on the standardized moments of the wavelet coefficient distributions, is presented. An algorithm is proposed to compute this image characterization almost instantly for every possible separable or nonseparable wavelet filter. Therefore, using a different wavelet basis for each query image does not considerably increase computation times. On the other hand, significant retrieval performance increases were obtained in a medical image data set, a texture data set, a face recognition data set, and an object picture data set. This additional flexibility in wavelet adaptation paves the way to relevance feedback on image characterization itself and not simply on the way image characterizations are combined.     

Kernels in structured multi-feature spaces for image retrieval

A new kernel for structured multi-feature spaces is introduced. It exploits the diversity of information encapsulated in different features. The mathematical validity of the introduced kernel is proven in the context of a conventional convex optimisation problem for support vector machines. Computer simulations show high performance for classification of images.     

用于图像检索的MPEG-7形状描述子

介绍了基于内容的图像检索技术的研究现状，并对将MPEG-7形状描述子应用于图像数据库进行图像检索进行了研究，最后提出了一些需要解决的问题和今后的研究方向。