Valuing Personal Data with Privacy Consideration.

A key challenge in information privacy research is how to value personal data with privacy consideration. In this study, we propose an experimental auction approach for valuing personal data. We use the generalized second-price auction to assess the monetary values of individuals' identity, demographic, and private information. We find that individuals' economic valuation of personal data is consistent with their actual self-disclosure behaviors. The economic valuation approach also produces results that are consistent with some well-accepted observations about consumer demographics and privacy. On the other hand, individuals' stated privacy preferences and attitudes are not consistent with their economic valuation. The findings of this study suggest that the proposed approach can be an effective mechanism for measuring personal data privacy. This study also provides important insights into valuing personal information for practical uses with several implications to policy decision makers, corporate executives and managers, data analysts, as well as decision science researchers.

Protecting Privacy When Sharing and Releasing Data with Multiple Records per Person.

This study concerns the risks of privacy disclosure when sharing and releasing a dataset in which each individual may be associated with multiple records. Existing data privacy approaches and policies typically assume that each individual in a shared dataset corresponds to a single record, leading to an underestimation of the disclosure risks in multiple records per person scenarios. We propose two novel measures of privacy disclosure to arrive at a more appropriate assessment of disclosure risks. The first measure assesses individual-record disclosure risk based upon the frequency distribution of individuals' occurrences. The second measure assesses sensitive-attribute disclosure risk based upon the number of individuals affiliated with a sensitive value. We show that the two proposed disclosure measures generalize the well-known k-anonymity and l-diversity measures, respectively, and work for scenarios with either a single record or multiple records per person. We have developed an efficient computational procedure that integrates the two proposed measures and a data quality measure to anonymize the data with multiple records per person when sharing and releasing the data for research and analytics. The results of the experimental evaluation using real-world data demonstrate the advantage of the proposed approach over existing techniques for protecting privacy while preserving data quality.

Predictive Analytics with Strategically Missing Data.

We study strategically missing data problems in predictive analytics with regression. In many real-world situations, such as financial reporting, college admission, job application, and marketing advertisement, data providers often conceal certain information on purpose in order to gain a favorable outcome. It is important for the decision-maker to have a mechanism to deal with such strategic behaviors. We propose a novel approach to handle strategically missing data in regression prediction. The proposed method derives imputation values of strategically missing data based on the Support Vector Regression models. It provides incentives for the data providers to disclose their true information. We show that with the proposed method imputation errors for the missing values are minimized under some reasonable conditions. An experimental study on real-world data demonstrates the effectiveness of the proposed approach.

One-Step Dip-Coating-Fabricated Core-Shell Silk Fibroin Rice Paper Fibrous Scaffolds for 3D Tumor Spheroid Formation.

Bioscaffolds are important substrates for supporting three-dimensional (3D) cell cultures. Silk fibroin (SF) is an attractive biomaterial in tissue engineering because of its good biocompatibility and mechanical properties. Electrospinning is one of the most often used approaches to fabricate SF fibrous scaffolds; yet, this technique still faces many challenges, such as low yield, residual organic solvents, limited extensibility of fibers, and a lack of spatial control over pore size. To circumvent these limitations, a core-shell SF on rice paper (SF@RP) fibrous scaffold was fabricated using a mild one-step dip-coating method. The cellulose fiber matrix of RP is the physical basis of the 3D scaffold, whereas the SF coating on the cellulose fiber controls the adhesion/spreading of the cells. The results indicated that by tuning the secondary structure of SF on the surface of a SF@RP scaffold, the cell behavior on SF@RP could be tuned. Tumor spheroids can be formed on SF@RP scaffolds with a dominant random secondary structure, in contrast to cells adhering and spreading on SF@RP scaffolds with a higher ratio of ß-sheet secondary structures. Direct culturing of breast cancer MDA-MB-231 and MCF-7, lung cancer A549, prostate cancer DU145, and liver cancer HepG2 cells could spontaneously lead to corresponding tumor spheroids on SF@RP. In addition, the physiological characteristics of HepG2 tumor spheroids were investigated, and the results showed that compared with HepG2 monolayer cells, CYP3A4, CYP1A1, and albumin gene expression levels in HepG2 cell spheres formed on SF@RP scaffolds were significantly higher. Moreover, these spheroids showed higher drug resistance. In summary, these SF@RP scaffolds prepared by the dip-coating method are biocompatible substrates for cell culture, especially for tumor cell spheroid formation.

Differential Privacy for the Vast Majority.

Differential privacy has become one of the widely used mechanisms for protecting sensitive information in databases and information systems. Although differential privacy provides a clear measure of privacy guarantee, it implicitly assumes that each individual corresponds to a single record in the result of a database query. This assumption may not hold in many database query applications. When an individual has multiple records, strict implementation of differential privacy may cause significant information loss. In this study, we extend the differential privacy principle to situations where multiple records in a database are associated with the same individual. We propose a new privacy principle that integrates differential privacy with the Pareto principle in analyzing privacy risk and data utility. When applied to the situations with multiple records per person, the proposed approach can significantly reduce the information loss in the released query results with a relatively small relaxation in the differential privacy guarantee. The effectiveness of the proposed approach is evaluated using three real-world databases.

Anonymizing 1:M microdata with high utility.

Preserving privacy and utility during data publishing and data mining is essential for individuals, data providers and researchers. However, studies in this area typically assume that one individual has only one record in a dataset, which is unrealistic in many applications. Having multiple records for an individual leads to new privacy leakages. We call such a dataset a 1:M dataset. In this paper, we propose a novel privacy model called (k, l)-diversity that addresses disclosure risks in 1:M data publishing. Based on this model, we develop an efficient algorithm named 1:M-Generalization to preserve privacy and data utility, and compare it with alternative approaches. Extensive experiments on real-world data show that our approach outperforms the state-of-the-art technique, in terms of data utility and computational cost.

Anonymizing and Sharing Medical Text Records.

Health information technology has increased accessibility of health and medical data and benefited medical research and healthcare management. However, there are rising concerns about patient privacy in sharing medical and healthcare data. A large amount of these data are in free text form. Existing techniques for privacy-preserving data sharing deal largely with structured data. Current privacy approaches for medical text data focus on detection and removal of patient identifiers from the data, which may be inadequate for protecting privacy or preserving data quality. We propose a new systematic approach to extract, cluster, and anonymize medical text records. Our approach integrates methods developed in both data privacy and health informatics fields. The key novel elements of our approach include a recursive partitioning method to cluster medical text records based on the similarity of the health and medical information and a value-enumeration method to anonymize potentially identifying information in the text data. An experimental study is conducted using real-world medical documents. The results of the experiments demonstrate the effectiveness of the proposed approach.

Preserving Patient Privacy When Sharing Same-Disease Data.

Medical and health data are often collected for studying a specific disease. For such same-disease microdata, a privacy disclosure occurs as long as an individual is known to be in the microdata. Individuals in same-disease microdata are thus subject to higher disclosure risk than those in microdata with different diseases. This important problem has been overlooked in data-privacy research and practice, and no prior study has addressed this problem. In this study, we analyze the disclosure risk for the individuals in same-disease microdata and propose a new metric that is appropriate for measuring disclosure risk in this situation. An efficient algorithm is designed and implemented for anonymizing same-disease data to minimize the disclosure risk while keeping data utility as good as possible. An experimental study was conducted on real patient and population data. Experimental results show that traditional reidentification risk measures underestimate the actual disclosure risk for the individuals in same-disease microdata and demonstrate that the proposed approach is very effective in reducing the actual risk for same-disease data. This study suggests that privacy protection policy and practice for sharing medical and health data should consider not only the individuals' identifying attributes but also the health and disease information contained in the data. It is recommended that data-sharing entities employ a statistical approach, instead of the HIPAA's Safe Harbor policy, when sharing same-disease microdata.

Unveiling consumer's privacy paradox behaviour in an economic exchange.

Privacy paradox is of great interest to IS researchers and firms gathering personal information. It has been studied from social, behavioural, and economic perspectives independently. However, prior research has not examined the degrees of influence these perspectives contribute to the privacy paradox problem. We combine both economic and behavioural perspectives in our study of the privacy paradox with a price valuation of personal information through an economic experiment combined with a behavioural study on privacy paradox. Our goal is to reveal more insights on the privacy paradox through economic valuation on personal information. Results indicate that general privacy concerns or individual disclosure concerns do not have a significant influence on the price valuation of personal information. Instead, prior disclosure behaviour in specific scenario, like with healthcare providers or social networks, is a better indicator of consumer price valuations.

Genome-wide Association Mapping of Quantitative Trait Loci (QTLs) for Contents of Eight Elements in Brown Rice (Oryza sativa L.).

An association mapping of quantitative trait loci (QTLs) regulating the concentrations of eight elements in brown rice (Oryza sativa L.) was performed using USDA mini-core subset cultivated in two different environments. In addition, correlation between the grain elemental concentrations was also studied. A total of 60 marker loci associated with 8 grain elemental concentrations were identified, and these loci were clustered into 37 genomic regions. Twenty new QTLs were found to be associated with important elements such as Zn, Fe, and P, along with others. Fe concentration was associated with the greatest number of markers in two environments. In addition, several important elemental/metal transporter genes were identified in a few mapped regions. Positive correlation was observed within all grain elemental concentrations. In summary, the results provide insight into the genetic basis of rice grain element accumulation and may help in the identification of genes associated with the accumulation of Zn, Fe, and other essential elements in rice.

Pricing and disseminating customer data with privacy awareness.

Organizations today regularly share their customer data with their partners to gain competitive advantages. They are also often requested or even required by a third party to provide customer data that are deemed sensitive. In these circumstances, organizations are obligated to protect the privacy of the individuals involved while still benefiting from sharing data or meeting the requirement for releasing data. In this study, we analyze the tradeoff between privacy and data utility from the perspective of the data owner. We develop an incentive-compatible mechanism for the data owner to price and disseminate private data. With this mechanism, a data user is motivated to reveal his true purpose of data usage and acquire the data that suits to that purpose. Existing economic studies of information privacy primarily consider the interplay between the data owner and the individuals, focusing on problems that occur in the collection of private data. This study, however, examines the privacy issue facing a data owner organization in the distribution of private data to a third party data user when the real purpose of data usage is unclear and the released data could be misused.

Digression and Value Concatenation to Enable Privacy-Preserving Regression.

Regression techniques can be used not only for legitimate data analysis, but also to infer private information about individuals. In this paper, we demonstrate that regression trees, a popular data-analysis and data-mining technique, can be used to effectively reveal individuals' sensitive data. This problem, which we call a "regression attack," has not been addressed in the data privacy literature, and existing privacy-preserving techniques are not appropriate in coping with this problem. We propose a new approach to counter regression attacks. To protect against privacy disclosure, our approach introduces a novel measure, called digression, which assesses the sensitive value disclosure risk in the process of building a regression tree model. Specifically, we develop an algorithm that uses the measure for pruning the tree to limit disclosure of sensitive data. We also propose a dynamic value-concatenation method for anonymizing data, which better preserves data utility than a user-defined generalization scheme commonly used in existing approaches. Our approach can be used for anonymizing both numeric and categorical data. An experimental study is conducted using real-world financial, economic and healthcare data. The results of the experiments demonstrate that the proposed approach is very effective in protecting data privacy while preserving data quality for research and analysis.

Class Restricted Clustering and Micro-Perturbation for Data Privacy.

The extensive use of information technologies by organizations to collect and share personal data has raised strong privacy concerns. To respond to the public's demand for data privacy, a class of clustering-based data masking techniques is increasingly being used for privacy-preserving data sharing and analytics. Traditional clustering-based approaches for masking numeric attributes, while addressing re-identification risks, typically do not consider the disclosure risk of categorical confidential attributes. We propose a new approach to deal with this problem. The proposed method clusters data such that the data points within a group are similar in the non-confidential attribute values whereas the confidential attribute values within a group are well distributed. To accomplish this, the clustering method, which is based on a minimum spanning tree (MST) technique, uses two risk-utility tradeoff measures in the growing and pruning stages of the MST technique respectively. As part of our approach we also propose a novel cluster-level micro-perturbation method for masking data that overcomes a common problem of traditional clustering-based methods for data masking, which is their inability to preserve important statistical properties such as the variance of attributes and the covariance across attributes. We show that the mean vector and the covariance matrix of the masked data generated using the micro-perturbation method are unbiased estimates of the original mean vector and covariance matrix. An experimental study on several real-world datasets demonstrates the effectiveness of the proposed approach.

Developing Privacy Solutions for Sharing and Analyzing Healthcare Data.

The extensive use of electronic health data has increased privacy concerns. While most healthcare organizations are conscientious in protecting their data in their databases, very few organizations take enough precautions to protect data that is shared with third party organizations. Recently the regulatory environment has tightened the laws to enforce privacy protection. The goal of this research is to explore the application of data masking solutions for protecting patient privacy when data is shared with external organizations for research, analysis and other similar purposes. Specifically, this research project develops a system that protects data without removing sensitive attributes. Our application allows high quality data analysis with the masked data. Dataset-level properties and statistics remain approximately the same after data masking; however, individual record-level values are altered to prevent privacy disclosure. A pilot evaluation study on large real-world healthcare data shows the effectiveness of our solution in privacy protection.

[Clinical observation of visual quality after implantation of toric intraocular lens].

OBJECTIVE: To observe the visual quality after implantation of Acrysof toric intraocular lens (IOL) in cataract patients. METHODS: Eighty eyes (60 patients) had implantation of Acrysof toric IOL in our hospital between Oct. 2009 and Sep. 2010. The patients were divided into four groups according to the toric models (T3, T4 and T5). The uncorrected visual acuity (UCVA), best corrected visual acuity (BCVA), residual cylinders, contrast sensitivity, high order aberration and IOL axis were evaluated preoperatively and 3 months postoperatively. Sixty-five eyes (45 patients) had implantation of Acrysof Natural IOL. RESULTS: The UCVA improved in the toric groups three months postoperatively. The UCVA of group T5b was lower than that of the other three groups. The UCVA of toric group was (0.19 ± 0.14) which was better than that of the control group (t = 4.04, P < 0.05). The residual cylinder was (1.69 ± 0.68) D in group T5b, which was statistically different from the other three groups. The residual cylinder of the toric group was (0.51 ± 0.78) D, which was much lower than the control group (t = 2.54, P < 0.05). The difference of contrast sensitivity between the four toric groups was not statistically significant. The low-order aberration and coma of group T5b was higher than that of the other three groups. The difference of contrast sensitivity between the toric group and the control group was not statistically significant. The low-order aberration and coma of the toric group were lower than those of the control group. The IOLs in 67 eyes (83.75%) rotated less than 5 degrees and all IOLs rotated less than 10 degrees. CONCLUSION: Implantation of the AcrySof toric IOL proves to be an effective, safe, and stable method of managing corneal astigmatism in cataract patients and provides good visual quality.

[Data mining for SSRs in ESTs and development of EST-SSR marker in oilseed rape].

Totally 2803 SSRs distributed in 2443 ESTs were mined out and accounted for 13.58% of 17987 non-redundant ESTs from oilseeed rape, with the average distance of distribution about 4.26 kb. Dinucleotide and trinucleotide repeats are the dominant type, with similar frequency and accounting for 89.05% together in all SSRs. AG/CT and AAG/CTT are the most frequent motifs, accounting for 84.31% and 37.71% in dinucleotide and trinucleotide repeats respectively. Further, 23 primer pairs for EST-SSRs were designed and the suitable annealing temperature for each primer pair was determined by gradient PCR. The amplification and polymorphism displayed by these primers in 10 varieties of oilseed rape were detected by using silver staining of nondenaturing polyacrylamide gel. 21 primer pairs showed the amplification, accounting for 91.30% of total primers, and 12 primer sets showed polymorphisms, accounting for 57.14% of primers available. These results indicate that it is an effective and feasible approach to develop SSR markers based on ESTs in oilseed rape.

Effects of behavioral and neurochemical changes on adult excitotoxic lesion of the ventral hippocampus.

BACKGROUND: The postmortem and magnetic resonance imaging studies for schizophrenic patients showed neuropathological abnormalities including neuron loss and volume reduction in ventral hippocampus (VH), some longitudinal studies suggest these changes may be a neurodegenerative process. OBJECTIVES: The present study examined the effects of adult bilateral VH lesions on a dopaminergic stimulant, methamphetamine (METH)-induced and an N-methyl-(D)-aspartate (NMDA) receptor antagonist, dizocilpine (MK-801)-induced behavioral and neurochemical changes in rats, in order to evaluate a potential of adult VH lesion animals for a model of schizophrenia. METHODS: To study the behavioral effects after bilateral VH lesions in adult rats, locomotor activity was measured individually by an infra-red sensor. Extracellular concentrations of dopamine in the nucleus accumbens (NAc) were measured using in vivo brain microdialysis. RESULTS: The bilateral adult VH lesion rats showed a significant enhanced hyperlocomotion in response to METH but no changes to MK-801 and phencyclidine; while bilateral adult VH lesion enhanced METH-induced increasing dopamine levels in the NAc. CONCLUSIONS: The bilateral adult VH lesions enhanced locomotor activity, which related to increased dopamine releases in the NAc, induced by a dopaminergic stimulant; these findings may suggest a potential of adult VH lesion animal for a model reflecting dopamine D2 receptor antagonist-responsive pathophysiology of schizophrenia by way of neurodegenerative processes.

Effect of a dopamine D1/5 receptor antagonist on haloperidol-induced inhibition of the acquisition of conditioned fear.

This study examined the effects of combined treatment with the typical antipsychotic drug haloperidol and dopamine D1/5 receptor antagonist SCH 23390 on the acquisition of contextual conditioned fear (re-exposure to an environment paired previously with inescapable electric footshocks), compared with those of various antipsychotic adjuvants, which may increase the effects of antipsychotic drugs. Rats were treated subcutaneously with haloperidol (3 mg/kg) combined with SCH 23390 (0.03 mg/kg) and were given fear conditioning by 5 min footshocks in shock chambers 30 min after the injection. One week after the footshocks, the rats were tested in the same shock chamber without shocks and freezing behavior was observed as an index of fear and anxiety. Haloperidol significantly inhibited the acquisition of conditioned freezing. SCH 23390 combined with haloperidol inhibited the acquisition of conditioned freezing more than either drug alone did. These results suggest that combined dopamine D2-like receptor antagonism and dopamine D1-like receptor antagonism is a promising and effective strategy to increase antipsychotic effects.

Selective serotonin reuptake inhibitor reduces conditioned fear through its effect in the amygdala.

Selective serotonin reuptake inhibitors are first-line treatment for most anxiety disorders, but their mechanism of anxiolytic action has not been clarified. Selective serotonin reuptake inhibitors are anxiolytic in conditioned fear stress (re-exposure to an environment paired previously with inescapable electric footshocks). To clarify the brain regions where selective serotonin reuptake inhibitors act, we examined the effect of microinjection of the selective serotonin reuptake inhibitor, citalopram, into the amygdala, medial prefrontal cortex and mediodorsal nucleus of the thalamus on freezing behavior, an index of fear, induced by conditioned fear stress. Bilateral injection of citalopram into the amygdala before testing reduced freezing significantly, while bilateral injection into the medial prefrontal cortex or mediodorsal nucleus of the thalamus did not. These results suggest that the anxiolytic effect of a selective serotonin reuptake inhibitor in conditioned fear is mediated by its effect in the amygdala, and support the hypothesis of serotonin function in anxiety by which facilitation of serotonin neurotransmission decreases anxiety.

[Biological basis of anxiety disorders and serotonergic anxiolytics].

Selective serotonin reuptake inhibitors (SSRIs) have wide indications for the treatment of anxiety disorders, including panic disorder, generalized anxiety disorder, posttraumatic stress disorder, obsessive-compulsive disorder and social anxiety disorder in addition to depression. Until recently, no animal model has been available for screening the anxiolytic effect of SSRIs and studying its mechanism of action. We have investigated the relationship between serotonin neurotransmission and anxiety using conditioned fear stress (CFS), an animal model of anxiety. CFS increased serotonin neurotransmission in the medial prefrontal cortex and amygdala. In behavioral pharmacological studies, SSRIs, serotonin1A agonists and monoamine oxidase inhibitors, which are assumed to facilitate serotonin neurotransmission, decreased conditioned freezing, an index of anxiety or fear, in CFS. In vivo microdialysis studies showed that serotonin neurotransmission in the medial prefrontal cortex increased after recovery from the freezing behavior. Microinjection of SSRI to the basolateral nucleus of the amygdala reduced conditioned freezing, indicating that the amygdala is one of target brain sites of anxiolytic action of SSRIs. Furthermore, CFS-induced c-Fos expression in the basolateral nucleus of the amygdala was reduced by SSRI pretreatment. Taken together, recent studies indicate that facilitation of brain serotonin neurotransmission decreases anxiety in agreement with the clinical evidence.