A deterministic approach for protecting privacy in sensitive personal data

BACKGROUND

Data privacy is one of the biggest challenges for any organisation which processes personal data, especially in the area of medical research where data include sensitive information about patients and study participants. Sharing of data is therefore problematic, which is at odds with the principle of open data that is so important to the advancement of society and science. Several statistical methods and computational tools have been developed to help data custodians and analysts overcome this challenge.

METHODS

In this paper, we propose a new deterministic approach for anonymising personal data. The method stratifies the underlying data by the categorical variables and re-distributes the continuous variables through a k nearest neighbours based algorithm.

RESULTS

We demonstrate the use of the deterministic anonymisation on real data, including data from a sample of Titanic passengers, and data from participants in the 1958 Birth Cohort.

CONCLUSIONS

The proposed procedure makes data re-identification difficult while minimising the loss of utility (by preserving the spatial properties of the underlying data); the latter means that informative statistical analysis can still be conducted.

Completeness of the operating room to intensive care unit handover: a matter of time?

BACKGROUND

Handovers of post-anesthesia patients to the intensive care unit (ICU) are often unstructured and performed under time pressure. Hence, they bear a high risk of poor communication, loss of information and potential patient harm. The aim of this study was to investigate the completeness of information transfer and the quantity of information loss during post anesthesia handovers of critical care patients.

METHODS

Using a self-developed checklist, including 55 peri-operative items, patient handovers from the operation room or post anesthesia care unit to the ICU staff were observed and documented in real time. Observations were analyzed for the amount of correct and completely transferred patient data in relation to the written documentation within the anesthesia record and the patient's chart.

RESULTS

During a ten-week study period, 97 handovers were included. The mean duration of a handover was 146 seconds, interruptions occurred in 34% of all cases. While some items were transferred frequently (basic patient characteristics [72%], surgical procedure [83%], intraoperative complications [93.8%]) others were commonly missed (underlying diseases [23%], long-term medication [6%]). The completeness of information transfer is associated with the handover's duration [B coefficient (95% CI): 0.118 (0.084-0.152), p<0.001] and increases significantly in handovers exceeding a duration of 2 minutes (24% ± 11.7 vs. 40% ± 18.04, p<0.001).

CONCLUSIONS

Handover completeness is affected by time pressure, interruptions, and inappropriate surroundings, which increase the risk of information loss. To improve completeness and ensure patient safety, an adequate time span for handover, and the implementation of communication tools are required.

The information isn't lost in gene expression

The present question is the possibility of information loss in gene expression? Information loss in the gene expression disrupts the cellular dynamics and can lead to serious defects, including cancer. Using Gottesman and Preskill method for calculating information loss in black holes,a mechanism for calculating the amount of information transformation in gene expression is proposed. In this proposal, there are three different Hilbert spaces that belong to degrees of freedom of DNA, RNA, and protein. The genetic sequence of the DNA is transcribed into protein at two stages. At first stage it is shown that the internal stationary state of the cell can be represented by a maximally entangled two-mode squeezed state of DNA and mRNA.At second stage, the state of the cell is described by a maximally entangled two-mode squeezed state of mRNA and protein. The amount of information transformation can be obtain by projecting the state at first stage on the state at second stage. Evidently for all finite values of the transcription factor concentration y, binding energy E and free energy F of the transcription factor, the information isn't lost in gene expression.

Majority rule has transition ratio 4 on Yule trees under a 2-state symmetric model

Inferring the ancestral state at the root of a phylogenetic tree from states observed at the leaves is a problem arising in evolutionary biology. The simplest technique - majority rule - estimates the root state by the most frequently occurring state at the leaves. Alternative methods - such as maximum parsimony - explicitly take the tree structure into account. Since either method can outperform the other on particular trees, it is useful to consider the accuracy of the methods on trees generated under some evolutionary null model, such as a Yule pure-birth model. In this short note, we answer a recently posed question concerning the performance of majority rule on Yule trees under a symmetric 2-state Markovian substitution model of character state change. We show that majority rule is accurate precisely when the ratio of the birth (speciation) rate of the Yule process to the substitution rate exceeds the value 4. By contrast, maximum parsimony has been shown to be accurate only when this ratio is at least 6. Our proof relies on a second moment calculation, coupling, and a novel application of a reflection principle.

An Approach to Reducing Information Loss and Achieving Diversity of Sensitive Attributes in k-anonymity Methods

Electronic Health Records (EHRs) enable the sharing of patients' medical data. Since EHRs include patients' private data, access by researchers is restricted. Therefore k-anonymity is necessary to keep patients' private data safe without damaging useful medical information. However, k-anonymity cannot prevent sensitive attribute disclosure. An alternative, l-diversity, has been proposed as a solution to this problem and is defined as: each Q-block (ie, each set of rows corresponding to the same value for identifiers) contains at least l well-represented values for each sensitive attribute. While l-diversity protects against sensitive attribute disclosure, it is limited in that it focuses only on diversifying sensitive attributes. The aim of the study is to develop a k-anonymity method that not only minimizes information loss but also achieves diversity of the sensitive attribute. This paper proposes a new privacy protection method that uses conditional entropy and mutual information. This method considers both information loss as well as diversity of sensitive attributes. Conditional entropy can measure the information loss by generalization, and mutual information is used to achieve the diversity of sensitive attributes. This method can offer appropriate Q-blocks for generalization. We used the adult database from the UCI Machine Learning Repository and found that the proposed method can greatly reduce information loss compared with a recent l-diversity study. It can also achieve the diversity of sensitive attributes by counting the number of Q-blocks that have leaks of diversity. This study provides a privacy protection method that can improve data utility and protect against sensitive attribute disclosure. The method is viable and should be of interest for further privacy protection in EHR applications.