Anonymisation of geographical distance matrices via Lipschitz embedding

BACKGROUND: Anonymisation of spatially referenced data has received increasing attention in recent years. Whereas the research focus has been on the anonymisation of point locations, the disclosure risk arising from the publishing of inter-point distances and corresponding anonymisation methods have not been studied systematically. METHODS: We propose a new anonymisation method for the release of geographical distances between records of a microdata file-for example patients in a medical database. We discuss a data release scheme in which microdata without coordinates and an additional distance matrix between the corresponding rows of the microdata set are released. In contrast to most other approaches this method preserves small distances better than larger distances. The distances are modified by a variant of Lipschitz embedding. RESULTS: The effects of the embedding parameters on the risk of data disclosure are evaluated by linkage experiments using simulated data. The results indicate small disclosure risks for appropriate embedding parameters. CONCLUSION: The proposed method is useful if published distance information might be misused for the re-identification of records. The method can be used for publishing scientific-use-files and as an additional tool for record-linkage studies

Evaluating Maintainability Prejudices with a Large-Scale Study of Open-Source Projects

Exaggeration or context changes can render maintainability experience into prejudice. For example, JavaScript is often seen as least elegant language and hence of lowest maintainability. Such prejudice should not guide decisions without prior empirical validation. We formulated 10 hypotheses about maintainability based on prejudices and test them in a large set of open-source projects (6,897 GitHub repositories, 402 million lines, 5 programming languages). We operationalize maintainability with five static analysis metrics. We found that JavaScript code is not worse than other code, Java code shows higher maintainability than C# code and C code has longer methods than other code. The quality of interface documentation is better in Java code than in other code. Code developed by teams is not of higher and large code bases not of lower maintainability. Projects with high maintainability are not more popular or more often forked. Overall, most hypotheses are not supported by open-source data.Comment: 20 page

A Protocol for the Secure Linking of Registries for HPV Surveillance

In order to monitor the effectiveness of HPV vaccination in Canada the linkage of multiple data registries may be required. These registries may not always be managed by the same organization and, furthermore, privacy legislation or practices may restrict any data linkages of records that can actually be done among registries. The objective of this study was to develop a secure protocol for linking data from different registries and to allow on-going monitoring of HPV vaccine effectiveness.A secure linking protocol, using commutative hash functions and secure multi-party computation techniques was developed. This protocol allows for the exact matching of records among registries and the computation of statistics on the linked data while meeting five practical requirements to ensure patient confidentiality and privacy. The statistics considered were: odds ratio and its confidence interval, chi-square test, and relative risk and its confidence interval. Additional statistics on contingency tables, such as other measures of association, can be added using the same principles presented. The computation time performance of this protocol was evaluated.The protocol has acceptable computation time and scales linearly with the size of the data set and the size of the contingency table. The worse case computation time for up to 100,000 patients returned by each query and a 16 cell contingency table is less than 4 hours for basic statistics, and the best case is under 3 hours.A computationally practical protocol for the secure linking of data from multiple registries has been demonstrated in the context of HPV vaccine initiative impact assessment. The basic protocol can be generalized to the surveillance of other conditions, diseases, or vaccination programs

De-identifying a public use microdata file from the Canadian national discharge abstract database

<p>Abstract</p> <p>Background</p> <p>The Canadian Institute for Health Information (CIHI) collects hospital discharge abstract data (DAD) from Canadian provinces and territories. There are many demands for the disclosure of this data for research and analysis to inform policy making. To expedite the disclosure of data for some of these purposes, the construction of a DAD public use microdata file (PUMF) was considered. Such purposes include: confirming some published results, providing broader feedback to CIHI to improve data quality, training students and fellows, providing an easily accessible data set for researchers to prepare for analyses on the full DAD data set, and serve as a large health data set for computer scientists and statisticians to evaluate analysis and data mining techniques. The objective of this study was to measure the probability of re-identification for records in a PUMF, and to de-identify a national DAD PUMF consisting of 10% of records.</p> <p>Methods</p> <p>Plausible attacks on a PUMF were evaluated. Based on these attacks, the 2008-2009 national DAD was de-identified. A new algorithm was developed to minimize the amount of suppression while maximizing the precision of the data. The acceptable threshold for the probability of correct re-identification of a record was set at between 0.04 and 0.05. Information loss was measured in terms of the extent of suppression and entropy.</p> <p>Results</p> <p>Two different PUMF files were produced, one with geographic information, and one with no geographic information but more clinical information. At a threshold of 0.05, the maximum proportion of records with the diagnosis code suppressed was 20%, but these suppressions represented only 8-9% of all values in the DAD. Our suppression algorithm has less information loss than a more traditional approach to suppression. Smaller regions, patients with longer stays, and age groups that are infrequently admitted to hospitals tend to be the ones with the highest rates of suppression.</p> <p>Conclusions</p> <p>The strategies we used to maximize data utility and minimize information loss can result in a PUMF that would be useful for the specific purposes noted earlier. However, to create a more detailed file with less information loss suitable for more complex health services research, the risk would need to be mitigated by requiring the data recipient to commit to a data sharing agreement.</p

Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation

Background Techniques have been developed to compute statistics on distributed datasets without revealing private information except the statistical results. However, duplicate records in a distributed dataset may lead to incorrect statistical results. Therefore, to increase the accuracy of the statistical analysis of a distributed dataset, secure deduplication is an important preprocessing step. Methods We designed a secure protocol for the deduplication of horizontally partitioned datasets with deterministic record linkage algorithms. We provided a formal security analysis of the protocol in the presence of semi-honest adversaries. The protocol was implemented and deployed across three microbiology laboratories located in Norway, and we ran experiments on the datasets in which the number of records for each laboratory varied. Experiments were also performed on simulated microbiology datasets and data custodians connected through a local area network. Results The security analysis demonstrated that the protocol protects the privacy of individuals and data custodians under a semi-honest adversarial model. More precisely, the protocol remains secure with the collusion of up to N − 2 corrupt data custodians. The total runtime for the protocol scales linearly with the addition of data custodians and records. One million simulated records distributed across 20 data custodians were deduplicated within 45 s. The experimental results showed that the protocol is more efficient and scalable than previous protocols for the same problem. Conclusions The proposed deduplication protocol is efficient and scalable for practical uses while protecting the privacy of patients and data custodians

The re-identification risk of Canadians from longitudinal demographics

<p>Abstract</p> <p>Background</p> <p>The public is less willing to allow their personal health information to be disclosed for research purposes if they do not trust researchers and how researchers manage their data. However, the public is more comfortable with their data being used for research if the risk of re-identification is low. There are few studies on the risk of re-identification of Canadians from their basic demographics, and no studies on their risk from their longitudinal data. Our objective was to estimate the risk of re-identification from the basic cross-sectional and longitudinal demographics of Canadians.</p> <p>Methods</p> <p>Uniqueness is a common measure of re-identification risk. Demographic data on a 25% random sample of the population of Montreal were analyzed to estimate population uniqueness on postal code, date of birth, and gender as well as their generalizations, for periods ranging from 1 year to 11 years.</p> <p>Results</p> <p>Almost 98% of the population was unique on full postal code, date of birth and gender: these three variables are effectively a unique identifier for Montrealers. Uniqueness increased for longitudinal data. Considerable generalization was required to reach acceptably low uniqueness levels, especially for longitudinal data. Detailed guidelines and disclosure policies on how to ensure that the re-identification risk is low are provided.</p> <p>Conclusions</p> <p>A large percentage of Montreal residents are unique on basic demographics. For non-longitudinal data sets, the three character postal code, gender, and month/year of birth represent sufficiently low re-identification risk. Data custodians need to generalize their demographic information further for longitudinal data sets.</p

A Systematic Review of Re-Identification Attacks on Health Data

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.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.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

Routes for breaching and protecting genetic privacy

We are entering the era of ubiquitous genetic information for research, clinical care, and personal curiosity. Sharing these datasets is vital for rapid progress in understanding the genetic basis of human diseases. However, one growing concern is the ability to protect the genetic privacy of the data originators. Here, we technically map threats to genetic privacy and discuss potential mitigation strategies for privacy-preserving dissemination of genetic data.Comment: Draft for comment