A Secure Data Enclave and Analytics Platform for Social Scientists

Data-driven research is increasingly ubiquitous and data itself is a defining asset for researchers, particularly in the computational social sciences and humanities. Entire careers and research communities are built around valuable, proprietary or sensitive datasets. However, many existing computation resources fail to support secure and cost-effective storage of data while also enabling secure and flexible analysis of the data. To address these needs we present CLOUD KOTTA, a cloud-based architecture for the secure management and analysis of social science data. CLOUD KOTTA leverages reliable, secure, and scalable cloud resources to deliver capabilities to users, and removes the need for users to manage complicated infrastructure.CLOUD KOTTA implements automated, cost-aware models for efficiently provisioning tiered storage and automatically scaled compute resources.CLOUD KOTTA has been used in production for several months and currently manages approximately 10TB of data and has been used to process more than 5TB of data with over 75,000 CPU hours. It has been used for a broad variety of text analysis workflows, matrix factorization, and various machine learning algorithms, and more broadly, it supports fast, secure and cost-effective research

UMCCTS Newsletter, December 2020

This is the December 2020 issue of the UMass Center for Clinical and Translational Science Newsletter containing news and events of interest

UMCCTS Newsletter, October 2020

This is the October 2020 issue of the UMass Center for Clinical and Translational Science Newsletter containing news and events of interest

Privacy Preserving Network Security Data Analytics: Architectures and System Design

An incessant rhythm of data breaches, data leaks, and privacy exposure highlights the need to improve control over potentially sensitive data. History has shown that neither public nor private sector organizations are immune. Lax data handling, incidental leakage, and adversarial breaches are all contributing factors. Prudent organizations should consider the sensitive nature of network security data. Logged events often contain data elements that are directly correlated with sensitive information about people and their activities -- often at the same level of detail as sensor data. Our intent is to produce a database which holds network security data representative of people\u27s interaction with the network mid-points and end-points without the problems of identifiability. In this paper we discuss architectures and propose a system design that supports a risk based approach to privacy preserving data publication of network security data that enables network security data analytics research

Administrative Transaction Data

"The value of administrative transaction data, such as financial transactions, credit card purchases, telephone calls, and retail store scanning data, to study social behaviour has long been recognised. Now new types of transactions data made possible by advances in cyber-technology have the potential to further exland social scientists’ research frontier. This chapter discusses the potential for such data to be included in the scientific infrastructure. It discusses new approaches to data dissemination, as well as the privacy and confidentiality issues raised by such data collection. It also discusses the characteristics of an optimal infrastructure to support the scientific analysis of transactions data." [author's abstract