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Despite evidence of structural brain abnormalities in schizophrenia, the current study aimed to explore the effects of antipsychotic treatment on gray matter (GM) volume using structural magnetic resonance imaging (MRI) and investigate the relationship between brain structure and treatment response. The GM volumes of 33 patients with first-episode schizophrenia were calculated with voxel-based morphometry (VBM), with 33 matched healthy controls. Longitudinal volume changes within subjects after 4-month antipsychotic treatment were also evaluated. Correlation between volumetric changes and clinical symptoms derived from the Positive and Negative Syndrome Scale (PANSS) were further investigated. Compared with healthy controls, decreased GM volumes in the frontal gyrus were observed in schizophrenia patients. After 4-month treatment, patients showed significantly decreased GM volume primarily in the bilateral frontal, temporal and left parietal brain regions. In addition, the GM volume changes of the left postcentral gyrus was positively correlated with negative symptoms improvement, and the correlation analysis revealed the total PANSS scores changes were associated with GM volume changes in the right inferior frontal gyrus and the right superior temporal gyrus. Besides, non-responders had reduced GM volume in the bilateral middle frontal gyrus and the right superior frontal gyrus compared with responders and healthy controls. Our results suggest that the abnormality in the right frontal gyrus exists in the early stage of schizophrenia. Moreover, the relationship between antipsychotics and structural changes was identified. The GM volume might have the potential to reflect the symptom improvement in schizophrenia patients. And MRI may assist in predicting the antipsychotic treatment response in first-episode schizophrenia patients.

Schizophrenia (SCZ) is an inherited disease, with the familial risk being among the most important factors when evaluating an individual’s risk for SCZ. However, robust imaging biomarkers for the disease that can be used for diagnosis and determination of the prognosis are lacking. Here, we explore the potential of functional connectivity (FC) for use as a biomarker for the early detection of high-risk first-degree relatives (FDRs). Thirty-eight first-episode SCZ patients, 38 healthy controls (HCs), and 33 FDRs were scanned using resting-state functional magnetic resonance imaging. The subjects’ brains were parcellated into 200 regions using the Craddock atlas, and the FC between each pair of regions was used as a classification feature. Multivariate pattern analysis using leave-one-out cross-validation achieved a correct classification rate of 88.15% [sensitivity 84.06%, specificity 92.18%, and area under the receiver operating characteristic curve (AUC) 0.93] for differentiating SCZ patients from HCs. FC located within the default mode, frontal-parietal, auditory, and sensorimotor networks contributed mostly to the accurate classification. The FC patterns of each FDR were input into each classification model as test data to obtain a corresponding prediction label (a total of 76 individual classification scores), and the averaged individual classification score was then used as a robust measure to characterize whether each FDR showed an SCZ-type or HC-type FC pattern. A significant negative correlation was found between the average classification scores of the FDRs and their semantic fluency scores. These findings suggest that FC combined with a machine learning algorithm could help to predict whether FDRs are likely to show an SCZ-specific or HC-specific FC pattern.

Investigations of brain structure in schizophrenia using magnetic resonance imaging (MRI) have identified variations in regional grey matter (GM) volume throughout the brain but the results are mixed. This study aims to investigate whether the inconsistent voxel-based morphometry (VBM) findings in schizophrenia are due to the use of different software packages. T1 MRI images were obtained from 86 first-episode schizophrenia (FESZ) patients and 86 age- and gender-matched Healthy controls (HCs). VBM analysis was carried out using FMRIB software library (FSL) 5.0 and statistical parametric mapping 8 (SPM8). All images were processed using the default parameter settings as provided by these software packages. FSL-VBM revealed widespread GM volume reductions in FESZ patients compared with HCs, however, for SPM-VBM, only increased and circumscribed GM volume changes were found, both software revealed increased GM volume within cerebellum. Significant correlations between Positive and Negative Syndrome Scale (PANSS) and GM volume were mainly found in frontal regions. Algorithms of GM tissue segmentation, image registration and statistical strategies might contribute to these disparate results.

Heterogeneous findings across studies of structural abnormality in schizophrenia (SZ) have impeded the development a unified theory of white matter pathology. As a cardinal symptom of SZ, auditory verbal hallucination (AVH) has been suspected to be associated with improper communication among several brain regions, which might indicated white matter pathology. Participants comprised 25 first-episode (FE) patients with AVH, 25 patients without AVH and 25 healthy subjects. Diffusion tensor imaging (DTI) measures were calculated using the TBSS of FSL. Voxel-based ANOVA tests were performed among the three groups and threshold-free cluster enhancement (TFCE) method correction was used for multiple comparisons. Voxel-based one-way ANOVA showed significant group effects for fractional anisotropy (FA) and radial diffusivity (RD) values. Post-hoc t-tests indicated that schizophrenia patients had lower FA and higher RD values in the internal capsule and anterior corona radiata than control subjects. Post-hoc analyses exhibited more widespread fiber disruptions in AVH patients than non-AVH patients. These results hinted on the important role of projection fiber disruption in schizophrenia patients. In addition, the current study also suggested that direct comparison between studies using patients with different symptom profiles should be interpreted with caution.

Data owners are expected to disclose micro-data for research, analysis, and various other purposes. In disclosing micro-data with sensitive attributes, the goal is usually two fold. First, the data utility of disclosed data should be maximized for analysis purposes. Second, the private information contained in such data must be to an acceptable level. Typically, a disclosure algorithm evaluates potential generalization functions in a predetermined order, and then discloses the first generalization that satisfies the desired privacy property. Recent studies show that adversarial inferences using knowledge about such disclosure algorithms can usually render the algorithm unsafe. In this paper, we show that an existing unsafe algorithm can be transformed into a large family of safe algorithms, namely, k-jump algorithms. We then prove that the data utility of different k-jump algorithms is generally incomparable. The comparison of data utility is independent of utility measures and syntactic privacy models. Finally, we analyze the computational complexity of k-jump algorithms, and confirm the necessity of safe algorithms even when a secret choice is made among algorithms.

Web-based applications are gaining popularity as they require less client-side resources, and are easier to deliver and maintain. On the other hand, web applications also pose new security and privacy challenges. In particular, recent research revealed that many high profile web applications might cause sensitive user inputs to be leaked from encrypted traffic due to side-channel attacks exploiting unique patterns in packet sizes and timing. Moreover, existing solutions, such as random padding and packet-size rounding, were shown to incur prohibitive overhead while still failing to guarantee sufficient privacy protection. In this paper, we first observe an interesting similarity between this privacy-preserving traffic padding (PPTP) issue and another well studied problem, privacy-preserving data publishing (PPDP). Based on such a similarity, we present a formal PPTP model encompassing the privacy requirements, padding costs, and padding methods. We then formulate PPTP problems under different application scenarios, analyze their complexity, and design efficient heuristic algorithms. Finally, we confirm the effectiveness and efficiency of our algorithms by comparing them to existing solutions through experiments using real-world web applications.

The small structures can directly affect the admissibility of the coal seam, the mining schedule, cost and safety. Seismic attributes extracted from seismic data can be used to identify small structures; However, geophysical methods often have multiple solutions, this will make the results of interpretation become unreliable. In this paper we will use image fusion technology to fuse a variety of seismic attributes, and then make full use of the complementarity of different attributes to reduce the multiple solutions, and make the seismic attributes more accurately in predicting small structures in coalmine.

The conventional inversion can only get P-impedance data, but neural network inversion can obtain various kinds of well-log attributes data by getting the nonlinear relationship through training the data of seismic attributes and well-log attributes, and then apply the nonlinear relationship to the whole seismic data volume. In this paper, we use multilayer feed forward neural network, probability neural network and radial basis function neural network to carry out the log-density inversion research, and obtain three pseudo density data volumes. We compare the effect of the inversion results and analysis the density distribution in spatial domain through section and slice data, at last we predict the stability of the coal seam’s floor in more reasonable way.

In this chapter, we study the side channel leak of sensitive micro-data in which adversaries combine the published data with their knowledge about the generalization algorithms used to produce such data, in order to refine their mental image about the sensitive data. Today, data owners are usually expected to disclose micro-data for research, analysis, and various other purposes. In disclosing micro-data with sensitive attributes, the goal is usually twofold. First, the data utility of disclosed data should be preserved to a certain level for analysis purposes. Second, the private information contained in such data must be sufficiently hidden. Typically, a disclosure algorithm would first sort potential generalization functions into a predetermined order (e.g., with decreasing utility), and then discloses data using the first generalization function that satisfies the desired privacy property. Knowledge about how such disclosure algorithms work can usually render the algorithm unsafe, because adversaries may refine their guesses of the sensitive data by “simulating” the algorithms and comparing with the disclosed data. In this chapter, we show that an existing unsafe algorithm can be transformed into a large family of safe algorithms, namely, k-jump algorithms. We then prove that the data utility of different k-jump algorithms is generally incomparable, which is independent of utility measures and privacy models. Finally, we analyze the computational complexity of k-jump algorithms, and confirm the necessity of safe algorithms even when a secret choice is made among algorithms.

In this chapter, we discuss how sensitive information about a household’s appliance status may be leaked from fine-grained smart meter readings. This is also an example of side channel leak because the readings are not supposed to serve as a channel for learning about individual appliances’ on/off status. While the features in smart grid, underpinned by the fine-grained usage information, provide significant benefits for both utility and customers, they also pose new security and privacy challenges. Existing solutions on privacy-preserving smart metering usually assume the readings to be sensitive and aim at protecting the readings through aggregation. In this chapter, we observe that the privacy issue in smart metering goes beyond the fine-grained readings themselves. That is, it may not be sufficient to simply focus on protecting such readings through aggregation or other techniques, without first understanding how such readings may lead to inferences of the truly sensitive information, that is, the appliance status. To address this issue, we present a formal model for privacy based on inferring appliance status from fine-grained meter readings.

While the strategy in previous chapter is theoretically superior to existing ones due to its independence of utility measures and privacy models, and its privacy guarantee under publicly-known algorithms, it incurs a high computational complexity. In this chapter, we study an efficient strategy for diversity preserving data publishing with publicly known algorithms (algorithms as side-channel). Our main observation is that a high computational complexity is usually incurred when an algorithm conflates the processes of privacy preservation and utility optimization. We then propose a novel privacy streamliner approach to decouple those two processes for improving algorithm efficiency. More specifically, we first identify a set of potential privacy-preserving solutions satisfying that an adversary’s knowledge about this set itself will not help him/her to violate the privacy property; we can then optimize utility within this set without worrying about privacy breaches since such an optimization is now simulatable by adversaries. To make our approach more concrete, we study it in the context of micro-data release with publicly known generalization algorithms. The analysis and experiments both confirm our algorithms to be more efficient than existing solutions.

The privacy preserving issues have received significant attentions in various domains. Various models and techniques have been proposed to achieve optimal privacy with minimal costs. However, side-channel leakages (such as, publicly-known algorithms of data publishing, observable traffic information in web application, fine-grained readings in smart metering) further complicate the process of privacy preservation. In this thesis, we make the first effort on investigating a general framework to model side-channel attacks across different domains and applying the framework to various categories of applications. In privacy-preserving data publishing with publicly-known algorithms, we first theoretically study a generic strategy independent of data utility measures and syntactic privacy properties. We then propose an efficient approach to preserving diversity. In privacy-preserving traffic padding in Web applications, we first propose a formal PPTP model to quantify the privacies and costs based on the key observation about the similarity between data publishing and traffic padding. We then introduce randomness into the previous solutions to provide background knowledge-resistant privacy guarantee. In privacy-preserving smart metering, we propose a light-weight approach to simultaneously preserving privacy on both billing and consumption aggregation based on the key observation about the privacy issue beyond the fine-grained readings.

Secure multi-party computation (SMC) allows multiple parties to jointly and securely compute a function while preserving the privacy of the involved parties. In this regard, homomorphic cryptosystems allow users to perform addition or multiplication operations on encrypted values without having to decrypt the input values. In this paper, we propose both a cryptographic and a non-cryptographic privacy-preserving protocols that allow one party to collaboratively compute a private polynomial function with at least two other parties using semantically secure cryptosystems. In this respect, the function to be evaluated is kept private to the party in question. In addition, we assume that there is no fully trusted party and that all parties are semi-honest. Moreover, we assume that there is no collusion between the different parties. We present the two protocols in question and report on the underlying performance through the presentation of our implementation results. The proposed protocols are relevant in many applications such as evidence sharing in multi-party forensic investigations, situation awareness through the secure sharing in distributed monitoring of plan execution, etc.

Studies comparing gray matter (GM) volume of schizophrenic patients with or without auditory verbal hallucinations (AVHs) to that of normal controls remain controversial. This project aims to investigate changes of GM volumes of drug-naïve schizophrenic patients with and without AVHs. Eighteen first episode schizophrenic (FES) patients with AVHs, 18 FES patients without AVHs and 18 healthy controls were scanned using structural MRI. Voxel-based morphometry (VBM) analysis was conducted to investigate changes of GM volume among the three groups. Patients with and without AVHs exhibited reduced GM volumes relative to normal controls in the left superior temporal gyrus, frontal regions, cerebellum and caudate. Further analysis of the GM of subcortical structures found that patients with AVHs had reduced thalamic volume than healthy controls. No significant difference was found between patients with and without AVHs. Significant correlation was found between the total scores of the Positive and Negative Syndrome Scale and bilateral thalamic volume. ROC analysis of thalamic volumes of the patients with AVHs and normal controls showed that the area under the curve was 0.698 (P = 0.043). The decreased thalamic volumes might serve as a biomarker for discriminating FES AVHs patients from normals.

While enjoying the convenience of Software as a Service (SaaS), users are also at an increased risk of privacy breaches. Recent studies show that a Web-based application may be inherently vulnerable to side-channel attacks which exploit unique packet sizes to identify sensitive user inputs from encrypted traffic. Existing solutions based on packet padding or packet-size rounding generally rely on the assumption that adversaries do not possess prior background knowledge about possible user inputs. In this paper, we propose a novel random ceiling padding approach whose results are resistant to such adversarial knowledge. Specifically, the approach injects randomness into the process of forming padding groups, such that an adversary armed with background knowledge would still face sufficient uncertainty in estimating user inputs. We formally present a generic scheme and discuss two concrete instantiations. We then confirm the correctness and performance of our approach through both theoretic analysis and experiments with two real world applications.

In releasing data with sensitive information, a data owner usually has seemingly conflicting goals, including privacy preservation, utility optimization, and algorithm efficiency. In this paper, we observe that a high computational complexity is usually incurred when an algorithm conflates the processes of privacy preservation and utility optimization. We then propose a novel privacy streamliner approach to decouple those two processes for improving algorithm efficiency. More specifically, we first identify a set of potential privacy-preserving solutions satisfying that an adversary's knowledge about this set itself will not help him/her to violate the privacy property; we can then optimize utility within this set without worrying about privacy breaches since such an optimization is now simulatable by adversaries. To make our approach more concrete, we study it in the context of micro-data release with publicly known generalization algorithms. The analysis and experiments both confirm our algorithms to be more efficient than existing solutions.

While web-based applications are becoming increasingly ubiquitous, they also present new security and privacy challenges. In particular, recent research revealed that many high profile Web applications might cause private user information to leak from encrypted traffic due to side-channel attacks exploiting packet sizes and timing. Moreover, existing solutions, such as random padding and packet-size rounding, are shown to incur prohibitive cost while still not ensuring sufficient privacy protection. In this paper, we propose a novel k-indistinguishable traffic padding technique to achieve the optimal tradeoff between privacy protection and communication and computational cost. Specifically, we first present a formal model of the privacy-preserving traffic padding (PPTP). We then formulate PPTP problems under different application scenarios, analyze their complexity, and design efficient heuristic algorithms. Finally, we confirm the effectiveness and efficiency of our algorithms by comparing them to existing solutions through experiments using real-world Web applications.

While web-based applications are gaining popularity, they also pose new security challenges. In particular, Chen et al. recently revealed that many popular Web applications actually leak out highly sensitive data from encrypted traffic due to side-channel attacks using packet sizes and timing [1]. They further demonstrated that existing solutions usually incur a high overhead while still not guaranteeing privacy protection. In this paper, we observe a striking similarity between this issue and another well studied problem, privacy-preserving data publishing (PPDP). Based on such a similarity, we propose a formal model for privacy-preserving traffic padding (PPTP) that encompasses privacy requirement, padding cost, and padding methods.

In disclosing micro-data with sensitive attributes, the goal is usually two fold. First, the data utility of disclosed data should be maximized for analysis purposes. Second, the private information contained in such data must be limited to an acceptable level. Recent studies show that adversarial inferences using knowledge about a disclosure algorithm can usually render the algorithm unsafe. In this paper, we show that an existing unsafe algorithm can be transformed into a large family of distinct safe algorithms, namely, k-jump algorithms. We prove that the data utility of different k-jump algorithms is generally incomparable. Therefore, a secret choice can be made among all k-jump algorithms to eliminate adversarial inferences while improving the data utility of disclosed micro-data.