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With plenty valuable information, open data are often deemed as great assets to academia or industry. In spite of some de-identification processing that most of data owners will perform before releasing the data, the more datasets are opened to public, the more likely personal privacy will be exposed. According to previous real case studies, even though the personally identifiable information has been de-identified, sensitive personal information could still be uncovered by heterogeneous or cross-domain data joining operations. The involved privacy re-identification processes are usually too complicated or obscure to be realized by data owners, not to mention that this problem will be more severe as the scale of data will get larger and larger. For preventing the leakage of sensitive information, this paper shows how to use a novel visualization analysis tool for open data de-identification (ODD Visualizer) to verify whether there exists sensitive information leakage problem in the target datasets. The high effectiveness that the ODD Visualizer can provide mainly comes from implementing a scalable computing platform as well as developing an efficient data visualization technique. Our demonstrations show that the ODD Visualizer can indeed uncover real vulnerability of record linkage attacks among open datasets available on the Internet.

Recognition of cyber threats from open threat intelligence can give advantages to incident response in very early stage. Previous related studies mostly focus on finding general hot terms instead of specific continuously changing targets; as a result, usage of these methods may be limited when given specific theme as default. To our best knowledge so far, the proposed Sec-Buzzer is the first web-based service not only finding the various emerging topics of cyber threats and its corresponding annotations (i.e., nearly zero-day attacks) but also providing the possible remedy solutions. Unlike previous works, Sec-Buzzer leverages different kinds of open source, Twitter and domain-specific blogs, and benefits a lot from the community-oriented filtering strategy as well as novel topic-association graph. Therefore, a set of highly contributing Twitter users will be grouped and scored as an expert community, and information from that will be explored then efficiently exploited. Demonstrations show that, by combining several measurements to quantify significances of experts and terms, Sec-Buzzer indeed uncovers unseen valuable domain experts to be information providers, as well as identifies emerging (or suddenly appearing) topics which are highly related to real security events happened recently.

Several common file synchronization services (such as GoogleDrive, Dropbox and so on) are employed as infrastructure for being used by command and control(C&C) and data exfiltration, saying Man-in-the-Cloud (MITC) attacks. MITC is not easily detected by common security measures result in without using any exploits, and re-configuration of these services can easily turn them into an attack tool. In this study, we propose Interactive Visualization Threats Explorer that can be with intuition to aware the potential cloud threats hiding in data and eventually improve the analyzing effectiveness significantly. Drill-down and quick response visualization analytics provides cloud administrators full and deep views between cloud resources and users behavior. In addition, Collaborative Risk Estimator which considers users social and business workflow behavior enhance analysis performance. By learning from past behavior of an individual user and social network relations, rolling up behavior models to continue adapt enterprise environment changes. Analyst can quickly aware high risk access behavior locality from abnormal cloud resource access and drill-down the unusual patterns and access behavior. To illustrate the effectiveness of this approach, we present example explorations on two real-world data sets for the detection and understanding of potential Advanced Persistent Threats in progress.

Opening data of plenty valuable information as public dataset provides great potential treasure to academy or industry. Despite of de-identification process that most of data owner will take before releasing those data, however, the more datasets are opened to public, the more likely personal privacy exposed will be. Previous studies have shown that personal identity and sensitive information might be re-identified by joining two or more de-identified data table with common attributes. According to previous real case studies, even though the personally identifiable information have been de-identified, sensitive personal information still could be uncovered by heterogeneous or cross-domain data joining operation. This kind of privacy re-identification are usually too complicated or obscure to be realized by data owner, not to mention that this problem will be more severe as the scale of data goes large. For the purpose of preventing damage of sensitive information leakage, this paper shows how to use a novel open data de-identification visualization analysis tool (ODD Visualizer) to verify whether there exists sensitive information leakage problem in the target datasets. The high effectiveness, that ODD Visualizer can provide, mainly comes from implementing scalable computing platform as well as developing efficient data visualization technique. Demonstration proves that ODD Visualizer indeed uncovered one real vulnerability of record linkage attack among open datasets available on the internet.

Objective: In published studies, Y-box binding protein-1 (YB-1) correlated with the prognosis of patients with breast cancer (BC), but the specific role of YB-1 is still unclear. Our study aimed to evaluate the prognostic value of YB-1 in BC patients using meta-analysis based on the published studies. Methods: We searched the relevant literatures deadline for June 2014 in databases, including PubMed, Embase, Medline and Cochrane library, and finally 8 studies were included in our study. Our study contained 1094 BC patients with 398 YB-1 positive and 696 YB-1 negative. Results: Our results showed that YB-1 abnormal expression did not correlated with the lymph node status [OR = 1.258, 95% CI = 0.895-1.769, P = 0.186], high histological grade [OR = 2.709, 95% CI = 0.861-8.530, P = 0.089], histological type [OR = 0.837, 95% CI = 0.526-1.331, P = 0.452], P53 status [OR = 2.006, 95% CI 0.686-5.865, P = 0.203] and PR [OR = 0.607, 95% CI = 0.347-1.061, P = 0.080] in BC patients. But YB-1 over-expression was associated with other unfavorable factors: ER negativity [OR = 0.604, 95% CI = 0.388-0.941, P = 0.026], HER2 positivity [OR = 3.841, 95% CI = 2.637-5.594, P = 0.000], and high tumorous T stage [OR = 2.169, 95% CI = 1.295-3.632, P = 0.003]. In addition, our data suggested that high YB-1 expression had an adverse impact on 5-year OS [RR = 2.767, 95% CI = 2.054-3.727, P = 0.000] in BC patients. Conclusions: Our findings implied that YB-1 might a novel biomarker to predict the prognosis of BC, and could be a potential direction for developing diagnostic and therapeutic approaches in BC.