OBJECTIVES: We present SHARE, a new system for statistical health information release with differential privacy. We present two case studies that evaluate the software on real medical datasets and demonstrate the feasibility and utility of applying the differential privacy framework on biomedical data. MATERIALS AND METHODS: SHARE releases statistical information in electronic health records with differential privacy, a strong privacy framework for statistical data release. It includes a number of state-of-the-art methods for releasing multidimensional histograms and longitudinal patterns. We performed a variety of experiments on two real datasets, the surveillance, epidemiology and end results (SEER) breast cancer dataset and the Emory electronic medical record (EeMR) dataset, to demonstrate the feasibility and utility of SHARE. RESULTS: Experimental results indicate that SHARE can deal with heterogeneous data present in medical data, and that the released statistics are useful. The Kullback-Leibler divergence between the released multidimensional histograms and the original data distribution is below 0.5 and 0.01 for seven-dimensional and three-dimensional data cubes generated from the SEER dataset, respectively. The relative error for longitudinal pattern queries on the EeMR dataset varies between 0 and 0.3. While the results are promising, they also suggest that challenges remain in applying statistical data release using the differential privacy framework for higher dimensional data. CONCLUSIONS: SHARE is one of the first systems to provide a mechanism for custodians to release differentially private aggregate statistics for a variety of use cases in the medical domain. This proof-of-concept system is intended to be applied to large-scale medical data warehouses.

We demonstrate DPCube, a component in our Health Information DE-identification (HIDE) framework, for releasing differentially private data cubes (or multi-dimensional histograms) for sensitive data. HIDE is a framework we developed for integrating heterogenous structured and unstructured health information and provides methods for privacy preserving data publishing. The DPCube component uses differentially private access mechanisms and an innovative 2-phase multidimensional partitioning strategy to publish a multi-dimensional data cube or histogram that achieves good utility while satisfying differential privacy. We demonstrate that the released data cubes can serve as a sanitized synopsis of the raw database and, together with an optional synthesized dataset based on the data cubes, can support various Online Analytical Processing (OLAP) queries and learning tasks.

The popularity of Wikipedia and other online knowledge bases has recently produced an interest in the machine learning community for the problem of automatic linking. Automatic hyperlinking can be viewed as two sub problems - link detection which determines the source of a link, and link disambiguation which determines the destination of a link. Wikipedia is a rich corpus with hyperlink data provided by authors. It is possible to use this data to train classifiers to be able to mimic the authors in some capacity. In this paper, we introduce automatic link detection as a sequence labeling problem. Conditional random fields (CRFs) are a probabilistic framework for labeling sequential data. We show that training a CRF with different types of features from the Wikipedia dataset can be used to automatically detect links with almost perfect precision and high recall.

Collaborative online encyclopedias or knowledge bases such as Wikipedia and PlanetMath are becoming increasingly popular. We demonstrate NNexus, a generalization of the automatic linking engine of PlanetMath.org and the first system that automates the process of linking disparate encyclopedia entries into a fully-connected conceptual network. The main challenges of this problem space include: 1) linking quality (correctly identifying which terms to link and which entry to link to with minimal effort on the part of users), 2) efficiency and scalability, and 3) generalization to multiple knowledge bases and web-based information environment. We present NNexus that utilizes subject classification and other metadata to address these challenges and demonstrate its effectiveness and efficiency through multiple real world corpora.

While there is an increasing need to share data that may contain personal information, such data sharing must preserve individual privacy without disclosing any identifiable information. A considerable amount of research in the data privacy community has been devoted to formalizing the notion of identifiability with many techniques for anonymization, but is focused exclusively on structured data. On the other hand, efforts on de-identifying medical text documents in the medical informatics community are highly specialized for specific document types or a subset of identifiers. In addition, they rely on simple identifier removal or grouping techniques and do not take advantage of the research developments in the data privacy community. We developed an integrated system, HIDE, for Heterogeneous Information DE-identification including structured and unstructured data utilizing existing anonymization techniques. We demonstrate a prototype of our system and show the effectiveness of our approach through a set of real data augmented with synthesized data.

In this paper, we introduce Noosphere Networked Entry eXtension and Unification System (NNexus), a generalization of the automatic linking engine of Noosphere (at PlanetMath.org) and the first system that automates the process of linking disparate "encyclopediardquo entries into a fully connected conceptual network. The main challenges of this problem space include: 1) linking quality (correctly identifying which terms to link and which entry to link to with minimal effort on the part of users), 2) efficiency and scalability, and 3) generalization to multiple knowledge bases and web-based information environment. We present the NNexus approach that utilizes subject classification and other metadata to address these challenges. We also present evaluation results demonstrating the effectiveness and efficiency of the approach and discuss ongoing and future directions of research.

While there is an increasing need to share medical information for public health research, such data sharing must preserve patient privacy without disclosing any identifiable information. A considerable amount of research in data privacy community has been devoted to formalizing the notion of identifiability and developing techniques for anonymization but are focused exclusively on structured data. On the other hand, efforts on de-identifying medical text documents in medical informatics community rely on simple identifier removal or grouping techniques without taking advantage of the research developments in the data privacy community. This paper attempts to fill the above gaps and presents a prototype system for de-identifying health information including both structured and unstructured data. It deploys a conditional random fields based technique for extracting identifying attributes from unstructured data and k-anonymization based technique for de-identifying the data while preserving maximum data utility. We present a set of preliminary evaluations showing the effectiveness of our approach.

Collaborative online encyclopedias such as Wikipedia and PlanetMath are becoming increasingly popular. In order to understand an article in a corpus a user must understand the related and underlying concepts through linked articles. In this paper, we introduce NNexus, a generalization of the automatic linking component of PlanetMath.org and the first system that automates the process of linking encyclopedia entries into a semantic network of concepts. We discuss the challenges, present the conceptual models as well as specific mechanisms of NNexus system, and discuss some of our ongoing and completed works