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Persistent malware use techniques, such as obfuscation, process injection, and system call abuse to evade security mechanisms and avoid detection throughout their compromise. Malware analysis and memory forensics must have proper skill for fighting them. To show the limitation of current memory forensics, we introduce an adversarial technique to remove the forensics evidence required to identify malware, called parent process termination (PPT). PPT neither creates a new malware nor does it manipulate the features of a running process like malware obfuscation techniques, which abuse the parent–child relationship. In PPT, the malware process creates child processes for a malicious purpose and then terminates. This termination, letting the operating system (OS) reuses the parent process's resources and thus erases all trace of it, while leaving its children to perform anomalous activities. To show PPT's applicability in Windows OS, we run and analyze selected malware samples in a controlled environment. We implement PPT and show how this technique benefits from current memory forensics tools being unable to identify the exited processes. The forensics analysis proves behaviour of the PPT adversarial technique run in different malware executions. Our experiments show PPT successfully removes forensics evidence to identify the source of malicious activity. We hope these results can shed light on the future design of memory forensics tools and better-informed choices by users. [ABSTRACT FROM AUTHOR]

Health informatics, using new information technology, provides a fruitful set of data resource and knowledge that is very useful for secondary data users and researchers in various health systems and applications. Privacy acts, on the other hand, prevent direct access to this information without patient's consent. Various solutions have been proposed such as data anonymization and de-identification, on-site analysis, and limited remote access, to preserve the data owner's privacy. Each of those approaches has different drawbacks and limitations. For instance, data de-identification will reduce data utility because of low precision of the final released data, and also it has a risk of data re-identification using available public data and background knowledge. On-site analysis has physical limitations, such as lack of data centers in every geographic area, and time-consuming procedures, such as background checks. Remote access increases security risks, and when data has to be pulled from multiple data resources, it requires patient consent for data disclosure. In this paper, we propose a set of privacy-preserving methods and techniques for some popular health statistical analysis methods. Using this set of secure protocols health researchers and other data users are able to issue their requests as some queries, and receive only the results of their queries from the data owners, while each data custodian can keep their sensitive data private. Proposed methods have been tested using sample data to illustrate the performance of the results in terms of computational and communication complexities. Security proof of the proposed protocols has also been provided as a proof of concept. [ABSTRACT FROM AUTHOR]

Background There is limited capacity to assess the comparative risks of medications after they enter the market. For rare adverse events, the pooling of data from multiple sources is necessary to have the power and sufficient population heterogeneity to detect differences in safety and effectiveness in genetic, ethnic and clinically defined subpopulations. However, combining datasets from different data custodians or jurisdictions to perform an analysis on the pooled data creates significant privacy concerns that would need to be addressed. Existing protocols for addressing these concerns can result in reduced analysis accuracy and can allow sensitive information to leak. Objective To develop a secure distributed multi-party computation protocol for logistic regression that provides strong privacy guarantees. Methods We developed a secure distributed logistic regression protocol using a single analysis center with multiple sites providing data. A theoretical security analysis demonstrates that the protocol is robust to plausible collusion attacks and does not allow the parties to gain new information from the data that are exchanged among them. The computational performance and accuracy of the protocol were evaluated on simulated datasets. Results The computational performance scales linearly as the dataset sizes increase. The addition of sites results in an exponential growth in computation time. However, for up to five sites, the time is still short and would not affect practical applications. The model parameters are the same as the results on pooled raw data analyzed in SAS, demonstrating high model accuracy. Conclusion The proposed protocol and prototype system would allow the development of logistic regression models in a secure manner without requiring the sharing of personal health information. This can alleviate one of the key barriers to the establishment of large-scale post-marketing surveillance programs. We extended the secure protocol to account for correlations among patients within sites through generalized estimating equations, and to accommodate other link functions by extending it to generalized linear models. [ABSTRACT FROM AUTHOR]