"TrID is an utility designed to identify file types from their binary signatures. While there are similar utilities with hard coded logic, TrID has no fixed rules. Instead, it's extensible and can be trained to recognize new formats in a fast and automatic way."

Just in case you thought crowdfunding was all about gadgets and indie movies.

Author(s): Posner, Miriam | Abstract: While much work on libraries and digital humanities has focused on how to train and encourage individual librarians, we have not paid enough attention to the administrative and institutional factors required to help these professionals succeed. This article outlines some common sources of frustration for library professionals engaged in digital humanities work and offers sketches of some library-based digital humanities programs that are working to address these challenges.

People's movements are highly predictable, researchers say, making it easy to identify most individuals from supposedly anonymized location datasets. As these datasets have valid uses, this is yet another reason why we need better regulation.

The companies that sell information about how much money you make — and whether you’re pregnant, divorced, or trying to lose weight — are facing new scrutiny.

“Because of the widespread publicity surrounding this study and that its findings may be used by states and state consortia in implementing the Common Core State Standards Initiative, the authors should make the test dataset publicly available for analysis,”

“Then they have this tempting thought: If only the data points would line up . . . ”

This page provides guidance about methods and approaches to achieve de-identification in accordance with the Health Insurance Portability and Accountability Act of 1996 (HIPAA) Privacy Rule. The guidance explains and answers questions regarding the two methods that can be used to satisfy the Privacy Rule’s de-identification standard: Expert Determination and Safe Harbor . This guidance is intended to assist covered entities to understand what is de-identification, the general process by which de-identified information is created, and the options available for performing de-identification.

Background Privacy legislation in most jurisdictions allows the disclosure of health data for secondary purposes without patient consent if it is de-identified. Some recent articles in the medical, legal, and computer science literature have argued that de-identification methods do not provide sufficient protection because they are easy to reverse. Should this be the case, it would have significant and important implications on how health information is disclosed, including: (a) potentially limiting its availability for secondary purposes such as research, and (b) resulting in more identifiable health information being disclosed. Our objectives in this systematic review were to: (a) characterize known re-identification attacks on health data and contrast that to re-identification attacks on other kinds of data, (b) compute the overall proportion of records that have been correctly re-identified in these attacks, and (c) assess whether these demonstrate weaknesses in current de-identification methods.   Methods and Findings Searches were conducted in IEEE Xplore, ACM Digital Library, and PubMed. After screening, fourteen eligible articles representing distinct attacks were identified. On average, approximately a quarter of the records were re-identified across all studies (0.26 with 95% CI 0.046–0.478) and 0.34 for attacks on health data (95% CI 0–0.744). There was considerable uncertainty around the proportions as evidenced by the wide confidence intervals, and the mean proportion of records re-identified was sensitive to unpublished studies. Two of fourteen attacks were performed with data that was de-identified using existing standards. Only one of these attacks was on health data, which resulted in a success rate of 0.00013.   Conclusions The current evidence shows a high re-identification rate but is dominated by small-scale studies on data that was not de-identified according to existing standards. This evidence is insufficient to draw conclusions about the efficacy of de-identification methods.

We provide evidence for the status quo in economics with respect to data sharing using a unique data set with 488 hand-collected observations randomly taken fro

Paper for the 2012 American Society for Information Science and Technology (ASIS&T) Annual Meeting. This paper discusses digital curation in the academic library job market.

A group of scientists have developed a system that amplifies tiny movements in videos, a technique that could have practical applications in fields as diverse as health care, manufacturing and law enforcement.