Quantifying Differential Privacy in Continuous Data Release Under Temporal Correlations.

Differential Privacy (DP) has received increasing attention as a rigorous privacy framework. Many existing studies employ traditional DP mechanisms (e.g., the Laplace mechanism) as primitives to continuously release private data for protecting privacy at each time point (i.e., event-level privacy), which assume that the data at different time points are independent, or that adversaries do not have knowledge of correlation between data. However, continuously generated data tend to be temporally correlated, and such correlations can be acquired by adversaries. In this paper, we investigate the potential privacy loss of a traditional DP mechanism under temporal correlations. First, we analyze the privacy leakage of a DP mechanism under temporal correlation that can be modeled using Markov Chain. Our analysis reveals that, the event-level privacy loss of a DP mechanism may increase over time. We call the unexpected privacy loss temporal privacy leakage (TPL). Although TPL may increase over time, we find that its supremum may exist in some cases. Second, we design efficient algorithms for calculating TPL. Third, we propose data releasing mechanisms that convert any existing DP mechanism into one against TPL. Experiments confirm that our approach is efficient and effective.

Medication Episode Construction Framework for Retrospective Database Analyses of Patients With Chronic Diseases.

OBJECTIVE: For chronic diseases, medical history reconstruction is essential for retrospective database analyses. One important aspect is determining which prescriptions belong to the same episode. However, a standard framework for this task is still lacking, particularly for multitherapy datasets. This paper presents a medication episode construction framework for the medical history of patients with chronic diseases. METHODS: Allen's relaxed temporal relations (i.e., temporal relations with time constraints relaxed by ) is used to define the consecutive prescription relations considering the patients' behavior. For example, patients occasionally arrive earlier or later than their appointment. RESULTS: influences the generation of stable periods (i.e., periods of time, at least three months, in which a medication is continuously taken by a patient). When using the lowest selected value (7 days), considerably fewer shorter stable periods (for durations less than 300 days) are produced and more longer stable periods are produced compared to cases without using . Furthermore, the results show that by using , regarding the number of events, where a stable period continues the previous stable period, decreases and the number of medication transition events available to be observed increases. CONCLUSION: Using in medication episode construction from multitherapy prescription datasets enables the longer expression of short-duration fragmented prescriptions and pruning repetitive prescriptions. SIGNIFICANCE: Our proposed framework is designed for multitherapy datasets, which has not been addressed by previous studies. The concept of relaxes the prescription relation against noise caused by the patient behavior and consequently provides a compact, but informative search space for observing medication transition events in a longitudinal analysis.

ConTPL: Controlling Temporal Privacy Leakage in Differentially Private Continuous Data Release.

In many real-world systems, such as Internet of Thing, sensitive data streams are collected and analyzed continually. To protect privacy, a number of mechanisms are designed to achieve ϵ-differential privacy for processing sensitive streaming data, whose privacy loss is rigorously controlled within a given parameter ϵ. However, most of the existing studies do not consider the effect of temporal correlations among the continuously generated data on the privacy loss. Our recent work reveals that, the privacy loss of a traditional DP mechanism (e.g., Laplace mechanism) may not be bounded by ϵ due to temporal correlations. We call such unexpected privacy loss Temporal Privacy Leakage (TPL). In this demonstration, we design a system, ConTPL, which is able to automatically convert an existing differentially private streaming data release mechanism into one bounding TPL within a specified level. ConTPL also provides an interactive interface and real-time visualization to help data curator understand and explore the effect of different parameters on TPL.

Quantifying Differential Privacy under Temporal Correlations.

Differential Privacy (DP) has received increasing attention as a rigorous privacy framework. Many existing studies employ traditional DP mechanisms (e.g., the Laplace mechanism) as primitives, which assume that the data are independent, or that adversaries do not have knowledge of the data correlations. However, continuous generated data in the real world tend to be temporally correlated, and such correlations can be acquired by adversaries. In this paper, we investigate the potential privacy loss of a traditional DP mechanism under temporal correlations in the context of continuous data release. First, we model the temporal correlations using Markov model and analyze the privacy leakage of a DP mechanism when adversaries have knowledge of such temporal correlations. Our analysis reveals that the privacy loss of a DP mechanism may accumulate and increase over time. We call it temporal privacy leakage. Second, to measure such privacy loss, we design an efficient algorithm for calculating it in polynomial time. Although the temporal privacy leakage may increase over time, we also show that its supremum may exist in some cases. Third, to bound the privacy loss, we propose mechanisms that convert any existing DP mechanism into one against temporal privacy leakage. Experiments with synthetic data confirm that our approach is efficient and effective.

Revealing oral medication patterns from reconstructed long-term medication history of type 2 diabetes.

With the growing attention to evidence-based medical guideline development, longitudinal analysis of Electronic Medical Records (EMR) has become a good tool for providing insight into and new knowledge on the existing therapy. For chronic diseases, longitudinal analysis of medication history plays a key role in reaching this goal. However, raw medication data in EMR are not suitable for longitudinal analysis for several reasons. First, many prescriptions have a short duration. Second, the prescription duration may have a gap or overlap with other prescription durations. Additionally, for diabetes cases, physicians must wait for a certain period to observe the effectiveness of the medication. However, the existing methods do not address these conditions. To tackle these issues, we propose a set of rules for medication episode reconstruction. We then apply the rules for longitudinal analysis on anonymous Type 2 diabetes patients' EMR provided by Kyoto University Hospital. The EMR span from 2000 to 2015. Two of our significant results are as follows: (1) our proposed medication episode reconstruction method is able to compress the search space into 23.83% compared to the raw data, and (2) the preliminary results show the benefits of the method in revealing the existing medication patterns over the years and unfamiliar therapy transition.

Highly enantioselective synthesis of atropisomeric anilide derivatives through catalytic asymmetric N-arylation: conformational analysis and application to asymmetric enolate chemistry.

In the presence of (R)-DTBM-SEGPHOS-Pd(OAc)(2) catalyst, N-arylation (aromatic amination) of various o-tert-butylanilides with p-iodonitrobenzene proceeds with high enantioselectivity (88-96% ee) to give atropisomeric N-(p-nitrophenyl)anilides having an N-C chiral axis in good yields. Atropisomeric anilide products highly prefer to exist as the E-rotamer which has trans-disposed o-tert-butylphenyl group and carbonyl oxygen. The application of the present catalytic enantioselective N-arylation to an intramolecular version gives atropisomeric lactam derivatives with high optical purity (92-98% ee). The reaction of the lithium enolate prepared from the atropisomeric anilide and lactam products with various alkyl halides gives alpha-alkylated products with high diastereoselectivity (diastereomer ratio = 13:1 to 46:1).

Efficient synthesis of optically active atropisomeric anilides through catalytic asymmetric N-arylation reaction.

In the presence of (R)-DTBM-SEGPHOS-Pd(OAc)2 catalyst, N-arylation of ortho-tert-butyl-NH-anilides with 4-nitroiodobenzene proceeds with high enantioselectivity (89-95% ee) to give optically active atropisomeric anilides possessing N-C chiral axis. Furthermore, the intramolecular version of the present catalytic asymmetric N-arylation gave atropisomeric lactams with high optical purity (94-96% ee).

The GeneAround GO viewer.

We have developed a system for visualizing the Gene Ontology((TM)) hierarchy. The graphical browser interactively displays diagrams of the inheritance relationship for each term to help understand the meanings of terms when handling gene annotation data.

Achievement of a 920-MHz high resolution NMR.

We have developed a 920-MHz NMR system and performed the proton NMR measurement of H(2)O and ethylbenzene using the superconducting magnet operating at 21.6 T (920 MHz for proton), which is the highest field produced by a superconducting NMR magnet in the persistent mode. From the NMR measurements, it is verified that both homogeneity and stability of the magnet have a specification sufficient for a high resolution NMR.