Results 1 -
3 of
3
Information security and privacy in healthcare: Current state of research.
- International Journal of Internet and Enterprise Management,
, 2010
"... Abstract: Information security and privacy in the healthcare sector is an issue of growing importance. The adoption of digital patient records, increased regulation, provider consolidation and the increasing need for information exchange between patients, providers and payers, all point towards the ..."
Abstract
-
Cited by 31 (5 self)
- Add to MetaCart
(Show Context)
Abstract: Information security and privacy in the healthcare sector is an issue of growing importance. The adoption of digital patient records, increased regulation, provider consolidation and the increasing need for information exchange between patients, providers and payers, all point towards the need for better information security. We critically survey the literature on information security and privacy in healthcare, published in information systems journals as well as many other related disciplines including health informatics, public health, law, medicine, the trade press and industry reports. In this paper, we provide a holistic view of the recent research and suggest new areas of interest to the information systems community. Keywords: information security; privacy; healthcare; research literature. Reference to this paper should be made as follows: Appari, A. and Eric Johnson, M. (2010) 'Information security and privacy in healthcare: current state of research', Int.
A Behavior-based Approach Towards Statistics-Preserving Network Trace
"... In modern network measurement research, there exists a clear and demonstrable need for open sharing of large-scale network traffic datasets between organizations. Beyond network measurement, many security-related fields, such as those focused on detecting new exploits or worm outbreaks, stand to ben ..."
Abstract
- Add to MetaCart
In modern network measurement research, there exists a clear and demonstrable need for open sharing of large-scale network traffic datasets between organizations. Beyond network measurement, many security-related fields, such as those focused on detecting new exploits or worm outbreaks, stand to benefit given the ability to easily correlate information between several different sources. Currently, the primary factor limiting such sharing is the risk of disclosing private information. While prior anonymization work has focused on traffic content, analysis based on statistical behavior patterns within network traffic has, so far, been under-explored. This thesis proposes a new behavior-based approach towards network trace source-anonymization, motivated by the concept of anonymity-by-crowds, and conditioned on the statistical similarity in host behavior. Novel time-series models for network traffic and kernel metrics for similarity are derived, and the problem is framed such that anonymity and statistics-preservation are congruent objectives in an unsupervised-learning problem. Source-anonymity is connected directly to the group size and homogeneity under this approach, and metrics for these properties are derived. Optimal segmentation of the population into anonymized groups is approximated with a graph-partitioning problem
unknown title
"... Abstract—Sharing of log data is a valuable step towards the improvement of network security. However, logs often contain sensitive information and organizations are hesitant to share them. Anonymization methods are used for increasing protection, lowering the disclosure risk to a level considered sa ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract—Sharing of log data is a valuable step towards the improvement of network security. However, logs often contain sensitive information and organizations are hesitant to share them. Anonymization methods are used for increasing protection, lowering the disclosure risk to a level considered safe. Accordingly, a metric for anonymity is necessary to quantitatively assess the risk before releasing log data. In this paper, we propose a general framework for estimating disclosure risk using conditional entropy between the original and the anonymized datasets. We demonstrate our approach using network log files. I.
