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Genomic medicine holds great promise for improving health care, but integrating searchable and actionable genetic data into electronic health records (EHRs) remains a challenge. Here we describe Neptune, a system for managing the interaction between a clinical laboratory and an EHR system during the clinical reporting process. We developed Neptune and applied it to two clinical sequencing projects that required report customization, variant reanalysis, and EHR integration. Neptune has been applied for the generation and delivery of over 15,000 clinical genomic reports. This work spans two clinical tests based on targeted gene panels that contain 68 and 153 genes respectively. These projects demanded customizable clinical reports that contained a variety of genetic data types including single-nucleotide variants (SNVs), copy-number variants (CNVs), pharmacogenomics, and polygenic risk scores. Two variant reanalysis activities were also supported, highlighting this important workflow. Methods are needed for delivering structured genetic data to EHRs. This need extends beyond developing data formats to providing infrastructure that manages the reporting process itself. Neptune was successfully applied on two high-throughput clinical sequencing projects to build and deliver clinical reports to EHR systems. The software is open source and available at https://gitlab.com/bcm-hgsc/neptune .

Hardware Trojan detection has been becoming an attentive research subject since the first Trojan in real-world hardware was found. A common way to activate a hardware Trojan is to send a command, and detecting those command-activated Trojan is one of the significant dimensions in securing hardware. In this paper, we propose a novel chip-free detection method, called Pruning Bytes Command Search (PBCS), which is a machine learning-based approach and can efficiently find out command-activate hardware Trojans. The proposed PBCS has been evaluated in experimental environments (via micro-controller) and real-world validations (on smart cards). Our approach also combines with novelty detection and outlier detection methods and examines effects on One-Class Support Vector Machine, Local Outlier Factor, and Isolation Forest as distinguishers in five scenes, respectively. The findings of the evaluation show that our approach is competent for searching unknown commands. Accuracy performance can be enhanced when proper distinguishers are selected. The results demonstrate that PBCS can successfully find out all executable commands in an uncertain parsing path hardware, which implies our approach is applicable in the complicated context.

Introduction Currently, one of the commonly used methods for disseminating electronic health record (EHR)-based phenotype algorithms is providing a narrative description of the algorithm logic, often accompanied by flowcharts. A challenge with this mode of dissemination is the potential for under-specification in the algorithm definition, which leads to ambiguity and vagueness. Methods This study examines incidents of under-specification that occurred during the implementation of 34 narrative phenotyping algorithms in the electronic Medical Record and Genomics (eMERGE) network. We reviewed the online communication history between algorithm developers and implementers within the Phenotype Knowledge Base (PheKB) platform, where questions could be raised and answered regarding the intended implementation of a phenotype algorithm. Results We developed a taxonomy of under-specification categories via an iterative review process between two groups of annotators. Under-specifications that lead to ambiguity and vagueness were consistently found across narrative phenotype algorithms developed by all involved eMERGE sites. Discussion and conclusion Our findings highlight that under-specification is an impediment to the accuracy and efficiency of the implementation of current narrative phenotyping algorithms, and we propose approaches for mitigating these issues and improved methods for disseminating EHR phenotyping algorithms.

The advancement of precision medicine requires new methods to coordinate and deliver genetic data from heterogeneous sources to physicians and patients. The eMERGE III Network enrolled >25,000 participants from biobank and prospective cohorts of predominantly healthy individuals for clinical genetic testing to determine clinically actionable findings. The network developed protocols linking together the 11 participant collection sites and 2 clinical genetic testing laboratories. DNA capture panels targeting 109 genes were used for testing of DNA and sample collection, data generation, interpretation, reporting, delivery, and storage were each harmonized. A compliant and secure network enabled ongoing review and reconciliation of clinical interpretations, while maintaining communication and data sharing between clinicians and investigators. A total of 202 individuals had positive diagnostic findings relevant to the indication for testing and 1,294 had additional/secondary findings of medical significance deemed to be returnable, establishing data return rates for other testing endeavors. This study accomplished integration of structured genomic results into multiple electronic health record (EHR) systems, setting the stage for clinical decision support to enable genomic medicine. Further, the established processes enable different sequencing sites to harmonize technical and interpretive aspects of sequencing tests, a critical achievement toward global standardization of genomic testing. The eMERGE protocols and tools are available for widespread dissemination.

The ultrasound (US) screening of the infant hip is vital for the early diagnosis of developmental dysplasia of the hip (DDH). The US diagnosis of DDH refers to measuring alpha and beta angles that quantify hip joint development. These two angles are calculated from key anatomical landmarks and structures of the hip. However, this measurement process is not trivial for sonographers and usually requires a thorough understanding of complex anatomical structures. In this study, we propose a multi-task framework to learn the relationships among landmarks and structures jointly and automatically evaluate DDH. Our multi-task networks are equipped with three novel modules. Firstly, we adopt Mask R-CNN as the basic framework to detect and segment key anatomical structures and add one landmark detection branch to form a new multi-task framework. Secondly, we propose a novel shape similarity loss to refine the incomplete anatomical structure prediction robustly and accurately. Thirdly, we further incorporate the landmark-structure consistent prior to ensure the consistency of the bony rim estimated from the segmented structure and the detected landmark. In our experiments, 1,231 US images of the infant hip from 632 patients are collected, of which 247 images from 126 patients are tested. The average errors in alpha and beta angles are 2.221 degrees and 2.899 degrees. About 93% and 85% estimates of alpha and beta angles have errors less than 5 degrees, respectively. Experimental results demonstrate that the proposed method can accurately and robustly realize the automatic evaluation of DDH, showing great potential for clinical application., Comment: Accepted by IEEE Journal of Biomedical and Health Informatics. 14 pages, 10 figures and 10 tables

Objective Feature engineering is a major bottleneck in phenotyping. Properly learned medical concept embeddings (MCEs) capture the semantics of medical concepts, thus are useful for retrieving relevant medical features in phenotyping tasks. We compared the effectiveness of MCEs learned from knowledge graphs and electronic healthcare records (EHR) data in retrieving relevant medical features for phenotyping tasks. Materials and Methods We implemented 5 embedding methods including node2vec, singular value decomposition (SVD), LINE, skip-gram, and GloVe with 2 data sources: (1) knowledge graphs obtained from the observational medical outcomes partnership (OMOP) common data model; and (2) patient-level data obtained from the OMOP compatible electronic health records (EHR) from Columbia University Irving Medical Center (CUIMC). We used phenotypes with their relevant concepts developed and validated by the electronic medical records and genomics (eMERGE) network to evaluate the performance of learned MCEs in retrieving phenotype-relevant concepts. Hits@k% in retrieving phenotype-relevant concepts based on a single and multiple seed concept(s) was used to evaluate MCEs. Results Among all MCEs, MCEs learned by using node2vec with knowledge graphs showed the best performance. Of MCEs based on knowledge graphs and EHR data, MCEs learned by using node2vec with knowledge graphs and MCEs learned by using GloVe with EHR data outperforms other MCEs, respectively. Conclusion MCE enables scalable feature engineering tasks, thereby facilitating phenotyping. Based on current phenotyping practices, MCEs learned by using knowledge graphs constructed by hierarchical relationships among medical concepts outperformed MCEs learned by using EHR data.

ObjectiveConcept identification is a major bottleneck in phenotyping. Properly learned medical concept embeddings (MCEs) have semantic meaning of the medical concepts, thus useful for feature engineering in phenotyping tasks. The objective of this study is to compare the effectiveness of MCEs learned by using knowledge graphs and EHR data for facilitating high-throughput phenotyping.Materials and MethodsWe investigated four MCEs learned from different data sources and methods. Knowledge-graphs were obtained from the Observational Medical Outcomes Partnership (OMOP) common data model. Medical concept co-occurrence statistics were obtained from Columbia University Irving Medical Center’s (CUIMC) OMOP database. Two embedding methods, node2vec and GloVe, were used to learn embeddings for medical concepts. We used phenotypes with their corresponding concepts generated and validated by the Electronic Medical Records and Genomics (eMERGE) network to evaluate the performance of learned MCEs in identifying phenotype-relevant concepts.ResultsPrecision@k% and Recall@k% in identifying phenotype-relevant concepts based on a single concept and multiple seed concepts were used to evaluate MCEs. Recall@500% and Precision@500% based on a single seed concept of MCE learned using the enriched knowledge graph were 0.64 and 0.13, compared to Recall@500% and Precision@500% of MCE learned using the hierarchical knowledge graph (0.61 and 0.12), 5-year windowed EHR (0.51 and 0.10), and visit-windowed EHR (0.46 and 0.09).ConclusionMedical concept embedding enables scalable identification of phenotype-relevant medical concepts, thereby facilitating high-throughput phenotyping. Knowledge graphs constructed by hierarchical relationships among medical concepts learn more effective MCEs, highlighting the need of more sophisticated use of big data to leverage MCEs for phenotyping.

As the physical security of hardware systems becomes more and more serious, a large number of physical attacks and countermeasures against on-chip cryptographic algorithms are proposed. Clock glitch injection is an easy-to-implement and effective fault type. This paper presents a novel clock glitch-based fault attack on hardware-implemented encryption algorithm called homological fault attack (HFA). It allows us to attack with coarse-grained clock glitches and can extract the key only by the plaintext and whether the encryption result is correct. At the same time, this paper carries out HFA experiment on AES-128 encryption algorithm implemented on FPGA in the real physical environment. Experimental results show that HFA can be used for serial and parallel implementation of AES hardware implementation. And this method can be easily extended to attack other block encryption algorithms.

Objective To contribute a conceptual framework for evaluating data suitability to satisfy the research needs of observational studies. Materials and Methods Suitability considerations were derived from a systematic literature review on researchers’ common data needs in observational studies and a scoping review on frequent clinical database design considerations, and were harmonized to construct a suitability conceptual framework using a bottom-up approach. The relationships among the suitability categories are explored from the perspective of 4 facets of data: intrinsic, contextual, representational, and accessible. A web-based national survey of domain experts was conducted to validate the framework. Results Data suitability for observational studies hinges on the following key categories: Explicitness of Policy and Data Governance, Relevance, Availability of Descriptive Metadata and Provenance Documentation, Usability, and Quality. We describe 16 measures and 33 sub-measures. The survey uncovered the relevance of all categories, with a 5-point Likert importance score of 3.9 ± 1.0 for Explicitness of Policy and Data Governance, 4.1 ± 1.0 for Relevance, 3.9 ± 0.9 for Availability of Descriptive Metadata and Provenance Documentation, 4.2 ± 1.0 for Usability, and 4.0 ± 0.9 for Quality. Conclusions The suitability framework evaluates a clinical data source’s fitness for research use. Its construction reflects both researchers’ points of view and data custodians’ design features. The feedback from domain experts rated Usability, Relevance, and Quality categories as the most important considerations.

Bitcoin is a digital currency payment system, which bases on the property of decentralization and anonymization of Blockchain. Researches on transaction deanonymization for the Bitcoin system may not associate anonymous transactions with the IP addresses (physical identity) of the originator accurately and may consume network resources excessively. In this paper, we propose an approach to obtain the originating transactions through analyzing the propagation information. We calculate a pattern matching score by combining the propagation pattern extraction and the node weight assignment. Through carrying out the experiments in the real Bitcoin system, we effectively match the originating transactions with the target node, which reaches a precision of 81.3% and is 30% higher than the state-of-the-art method.

Objective Coordination ellipsis is a linguistic phenomenon abound in medical text and is challenging for concept normalization because of difficulty in recognizing elliptical expressions referencing 2 or more entities accurately. To resolve this bottleneck, we aim to contribute a generalizable method to reconstruct concepts from medical coordinated elliptical expressions in a variety of biomedical corpora. Materials and Methods We proposed a graph-based representation model and built a pipeline to reconstruct concepts from coordinated elliptical expressions in medical text (RECEEM). There are 4 modules: (1) identify all possible candidate conjunct pairs from original coordinated elliptical expressions, (2) calculate coefficients for candidate conjuncts using the embedding model, (3) select the most appropriate decompositions by global optimization, and (4) rebuild concepts based on a pathfinding algorithm. We evaluated the pipeline’s performance on 2658 coordinated elliptical expressions from 3 different medical corpora (ie, biomedical literature, clinical narratives, and eligibility criteria from clinical trials). Precision, recall, and F1 score were calculated. Results The F1 scores for biomedical publications, clinical narratives, and research eligibility criteria were 0.862, 0.721, and 0.870, respectively. RECEEM outperformed 2 previously released methods. By incorporating RECEEM into 2 existing NLP tools, the F1 scores increased from 0.248 to 0.460 and from 0.287 to 0.630 on concept mapping of 1125 coordination ellipses. Conclusions RECEEM improves concept normalization for medical coordinated elliptical expressions in a variety of biomedical corpora. It outperformed existing methods and significantly enhanced the performance of 2 notable NLP systems for mapping coordination ellipses in the evaluation. The algorithm is open sourced online (https://github.com/chiyuan1126/RECEEM).

BACKGROUND: Manually curating standardized phenotypic concepts such as Human Phenotype Ontology (HPO) terms from narrative text in electronic health records (EHRs) is time consuming and error prone. Natural language processing (NLP) techniques can facilitate automated phenotype extraction and thus improve the efficiency of curating clinical phenotypes from clinical texts. While individual NLP systems can perform well for a single cohort, an ensemble-based method might shed light on increasing the portability of NLP pipelines across different cohorts. METHODS: We compared four NLP systems, MetaMapLite, MedLEE, ClinPhen and cTAKES, and four ensemble techniques, including intersection, union, majority-voting and machine learning, for extracting generic phenotypic concepts. We addressed two important research questions regarding automated phenotype recognition. First, we evaluated the performance of different approaches in identifying generic phenotypic concepts. Second, we compared the performance of different methods to identify patient-specific phenotypic concepts. To better quantify the effects caused by concept granularity differences on performance, we developed a novel evaluation metric that considered concept hierarchies and frequencies. Each of the approaches was evaluated on a gold standard set of clinical documents annotated by clinical experts. One dataset containing 1,609 concepts derived from 50 clinical notes from two different institutions was used in both evaluations, and an additional dataset of 608 concepts derived from 50 case report abstracts obtained from PubMed was used for evaluation of identifying generic phenotypic concepts only. RESULTS: For generic phenotypic concept recognition, the top three performers in the NYP/CUIMC dataset are union ensemble (F(1), 0.643), training-based ensemble (F(1), 0.632), and majority vote-based ensemble (F(1), 0.622). In the Mayo dataset, the top three are majority vote-based ensemble (F(1), 0.642), cTAKES (F(1), 0.615), and MedLEE (F(1), 0.559). In the PubMed dataset, the top three are majority vote-based ensemble (F(1), 0.719), training-based (F(1), 0.696) and MetaMapLite (F(1), 0.694). For identifying patient specific phenotypes, the top three performers in the NYP/CUIMC dataset are majority vote-based ensemble (F(1), 0.610), MedLEE (F(1), 0.609), and training-based ensemble (F(1), 0.585). In the Mayo dataset, the top three are majority vote-based ensemble (F(1), 0.604), cTAKES (F(1), 0.531) and MedLEE (F(1) 0.527). CONCLUSIONS: Our study demonstrates that ensembles of natural language processing can improve both generic phenotypic concept recognition and patient specific phenotypic concept identification over individual systems. Among the individual NLP systems, each individual system performed best when they were applied in the dataset that they were primary designed for. However, combining multiple NLP systems to create an ensemble can generally improve the performance. Specifically, the ensemble can increase the results reproducibility across different cohorts and tasks, and thus provide a more portable phenotyping solution compared to individual NLP systems.

Algorithms for identifying patients of interest from observational data must address missing and inaccurate data and are desired to achieve comparable performance on both administrative claims and electronic health records data. However, administrative claims data do not contain the necessary information to develop accurate algorithms for disorders that require laboratory results, and this omission can result in insensitive diagnostic code-based algorithms. In this paper, we tested our assertion that the performance of a diagnosis code-based algorithm for chronic kidney disorder (CKD) can be improved by adding other codes indirectly related to CKD (e.g., codes for dialysis, kidney transplant, suspicious kidney disorders). Following the best practices from Observational Health Data Sciences and Informatics (OHDSI), we adapted an electronic health record-based gold standard algorithm for CKD and then created algorithms that can be executed on administrative claims data and account for related data quality issues. We externally validated our algorithms on four electronic health record datasets in the OHDSI network. Compared to the algorithm that uses CKD diagnostic codes only, positive predictive value of the algorithms that use additional codes was slightly increased (47.4% vs. 47.9%–48.5% respectively). The algorithms adapted from the gold standard algorithm can be used to infer chronic kidney disorder based on administrative claims data. We succeeded in improving the generalizability and consistency of the CKD phenotypes by using data and vocabulary standardized across the OHDSI network, although performance variability across datasets remains. We showed that identifying and addressing coding and data heterogeneity can improve the performance of the algorithms.

Electronic Health Record (EHR) data represents a valuable resource for individualized prospective prediction of health conditions. Statistical methods have been developed to measure patient similarity using EHR data, mostly using clinical attributes. Only a handful of recent methods have combined clinical analytics with other forms of similarity analytics, and no unified framework exists yet to measure comprehensive patient similarity. Here, we developed a generic framework named Patient similarity based on Domain Fusion (PsDF). PsDF performs patient similarity assessment on each available domain data separately, and then integrate the affinity information over various domains into a comprehensive similarity metric. We used the integrated patient similarity to support outcome prediction by assigning a risk score to each patient. With extensive simulations, we demonstrated that PsDF outperformed existing risk prediction methods including a random forest classifier, a regression-based model, and a naïve similarity method, especially when heterogeneous signals exist across different domains. Using PsDF and EHR data extracted from the data warehouse of Columbia University Irving Medical Center, we developed two different clinical prediction tools for two different clinical outcomes: incident cases of end stage kidney disease (ESKD) and severe aortic stenosis (AS) requiring valve replacement. We demonstrated that our new prediction method is scalable to large datasets, robust to random missingness, and generalizable to diverse clinical outcomes.

OBJECTIVES/GOALS: There is an imperative need to initiate translational genetic studies of hidradenitis suppurativa (HS). Such work requires large cohorts and no HS registries exist. Precision medicine initiatives provide new resources and methods for efficiently constructing cohorts, but empirically informed best practice guidelines are needed. METHODS/STUDY POPULATION: Traditional methods for building cohorts rely on clinical encounters to identify patients and collect phenotype data. Precision medicine initiatives aim to decrease the time and cost of data collection by using alternative sources, including electronic health records (EHR) and remote collection of patient-reported data. The public’s use of the Internet to obtain and exchange health-related information coupled with the success of direct-to-consumer genetic companies suggests that it is feasible to remotely ascertain research participants for genetic studies. Importantly, Internet cohorts provide an opportunity to include research participants who are disconnected from healthcare, and thus remain hidden from research that relies on EHR or clinical services. RESULTS/ANTICIPATED RESULTS: First, to conduct studies in EHR we are developing an analytic pipeline for the automated extraction of an accurate HS diagnosis using natural language processing of clinical notes. In our preliminary work we are also using ICD codes to build cohorts in two EHR systems with and without linked genetic data. Second, we have developed Internet advertising campaigns for symptom-based recruitment. Informed consent and patient-reported data is collected on-line through a series of short surveys. Patients who complete the surveys and express interest in participating in genetic studies are sent saliva collection kits and return mailing material. Finally, we have established an HS biobank that has DNA from 300 participants identified through clinical services. Enrollment is on going. DISCUSSION/SIGNIFICANCE OF IMPACT: Our goal is to assemble an HS cohort that is large enough to power genetic discoveries. Our work is generating empirical evidence for precision medicine guidelines and will improve our knowledge about HS. The methods we are developing can be applied to efficiently create new cohorts for genetic studies of other diseases across different clinical areas.

Emerging applications such as the Internet of Things (IoT) promotes the development of lightweight cryptography. SPECK is a lightweight block cipher, specially designed for limited resource devices that was presented by National Security Agency. Nevertheless, before using SPECK in any practical application, protection against side-channel attacks must be paid attention to. In this paper, we take two attack positions into account and make effort to implement correlation power analysis on a naive software implementation of SPECK algorithm in the IoT application scenario. Our experimental results show that the real key fixed in the register can be successfully recovered when attack the XOR operations, while there is always an interference item that confuses the correct key when attack the modulo addition operation. Furthermore, we proposal a countermeasure against power attacks in the IoT application, and the protected SPECK only cost 53.01%, 6.27% and 318.18% of extra code, RAM and time, respectively.

ObjectiveCohort definition is a bottleneck for conducting clinical research and depends on subjective decisions by domain experts. Data-driven cohort definition is appealing but requires substantial knowledge of terminologies and clinical data models. Criteria2Query is a natural language interface that facilitates human-computer collaboration for cohort definition and execution using clinical databases.Materials and MethodsCriteria2Query uses a hybrid information extraction pipeline combining machine learning and rule-based methods to systematically parse eligibility criteria text, transforms it first into a structured criteria representation and next into sharable and executable clinical data queries represented as SQL queries conforming to the OMOP Common Data Model. Users can interactively review, refine, and execute queries in the ATLAS web application. To test effectiveness, we evaluated 125 criteria across different disease domains from ClinicalTrials.gov and 52 user-entered criteria. We evaluated F1 score and accuracy against 2 domain experts and calculated the average computation time for fully automated query formulation. We conducted an anonymous survey evaluating usability.ResultsCriteria2Query achieved 0.795 and 0.805 F1 score for entity recognition and relation extraction, respectively. Accuracies for negation detection, logic detection, entity normalization, and attribute normalization were 0.984, 0.864, 0.514 and 0.793, respectively. Fully automatic query formulation took 1.22 seconds/criterion. More than 80% (11+ of 13) of users would use Criteria2Query in their future cohort definition tasks.ConclusionsWe contribute a novel natural language interface to clinical databases. It is open source and supports fully automated and interactive modes for autonomous data-driven cohort definition by researchers with minimal human effort. We demonstrate its promising user friendliness and usability.

Electronic health record (EHR) algorithms for defining patient cohorts are commonly shared as free-text descriptions that require human intervention both to interpret and implement. We developed the Phenotype Execution and Modeling Architecture (PhEMA, http://projectphema.org) to author and execute standardized computable phenotype algorithms. With PhEMA, we converted an algorithm for benign prostatic hyperplasia, developed for the electronic Medical Records and Genomics network (eMERGE), into a standards-based computable format. Eight sites (7 within eMERGE) received the computable algorithm, and 6 successfully executed it against local data warehouses and/or i2b2 instances. Blinded random chart review of cases selected by the computable algorithm shows PPV ≥90%, and 3 out of 5 sites had >90% overlap of selected cases when comparing the computable algorithm to their original eMERGE implementation. This case study demonstrates potential use of PhEMA computable representations to automate phenotyping across different EHR systems, but also highlights some ongoing challenges.

SKINNY is a new lightweight tweakable block cipher family, which can compete to other lightweight cipher in terms of hardware or software implementations. While its theoretical security has been widely studied, little effort has been made to analyze its implementation security such as side-channel attacks protection. In this paper, we first give correlation power attack and its experiment on SKINNY. Then, a masking scheme for software SKINNY is proposed against side-channel attacks. Its implementation shows that the protected SKINNY only costs 8.42%, 183.27%, and 90.85% of extra code, time, and RAM, respectively.

Objective To study the effect on patient cohorts of mapping condition (diagnosis) codes from source billing vocabularies to a clinical vocabulary. Materials and Methods Nine International Classification of Diseases, Ninth Revision, Clinical Modification (ICD9-CM) concept sets were extracted from eMERGE network phenotypes, translated to Systematized Nomenclature of Medicine - Clinical Terms concept sets, and applied to patient data that were mapped from source ICD9-CM and ICD10-CM codes to Systematized Nomenclature of Medicine - Clinical Terms codes using Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) vocabulary mappings. The original ICD9-CM concept set and a concept set extended to ICD10-CM were used to create patient cohorts that served as gold standards. Results Four phenotype concept sets were able to be translated to Systematized Nomenclature of Medicine - Clinical Terms without ambiguities and were able to perform perfectly with respect to the gold standards. The other 5 lost performance when 2 or more ICD9-CM or ICD10-CM codes mapped to the same Systematized Nomenclature of Medicine - Clinical Terms code. The patient cohorts had a total error (false positive and false negative) of up to 0.15% compared to querying ICD9-CM source data and up to 0.26% compared to querying ICD9-CM and ICD10-CM data. Knowledge engineering was required to produce that performance; simple automated methods to generate concept sets had errors up to 10% (one outlier at 250%). Discussion The translation of data from source vocabularies to Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) resulted in very small error rates that were an order of magnitude smaller than other error sources. Conclusion It appears possible to map diagnoses from disparate vocabularies to a single clinical vocabulary and carry out research using a single set of definitions, thus improving efficiency and transportability of research.

Identity-based cryptographic algorithm SM9, which has become the main part of the ISO/IEC 14888-3/AMD1 standard in November 2017, employs the identities of users to generate public-private key pairs. Without the support of digital certificate, it has been applied for cloud computing, cyber-physical system, Internet of Things, and so on. In this paper, the implementation of SM9 algorithm and its Simple Power Attack (SPA) are discussed. Then, we present template attack and fault attack on SPA-resistant SM9. Our experiments have proved that if attackers try the template attack on an 8-bit microcontrol unit, the secret key can be revealed by enabling the device to execute one time. Fault attack even allows the attackers to obtain the 256-bit key of SM9 by performing the algorithm twice and analyzing the two different results. Accordingly, some countermeasures to resist the three kinds of attacks above are given.

Display Omitted We evaluate PSI-based identification of relationships between drugs and ADRs.PSI can incorporate relations and concepts and infer reasoning pathways.PSI is more predictive than a co-occurrence based model.Reasoning pathways suggest supporting evidence from literature for review. Pharmacovigilance involves continually monitoring drug safety after drugs are put to market. To aid this process; algorithms for the identification of strongly correlated drug/adverse drug reaction (ADR) pairs from data sources such as adverse event reporting systems or Electronic Health Records have been developed. These methods are generally statistical in nature, and do not draw upon the large volumes of knowledge embedded in the biomedical literature. In this paper, we investigate the ability of scalable Literature Based Discovery (LBD) methods to identify side effects of pharmaceutical agents. The advantage of LBD methods is that they can provide evidence from the literature to support the plausibility of a drug/ADR association, thereby assisting human review to validate the signal, which is an essential component of pharmacovigilance. To do so, we draw upon vast repositories of knowledge that has been extracted from the biomedical literature by two Natural Language Processing tools, MetaMap and SemRep. We evaluate two LBD methods that scale comfortably to the volume of knowledge available in these repositories. Specifically, we evaluate Reflective Random Indexing (RRI), a model based on concept-level co-occurrence, and Predication-based Semantic Indexing (PSI), a model that encodes the nature of the relationship between concepts to support reasoning analogically about drug-effect relationships. An evaluation set was constructed from the Side Effect Resource 2 (SIDER2), which contains known drug/ADR relations, and models were evaluated for their ability to "rediscover" these relations. In this paper, we demonstrate that both RRI and PSI can recover known drug-adverse event associations. However, PSI performed better overall, and has the additional advantage of being able to recover the literature underlying the reasoning pathways it used to make its predictions.

Background Implementation of phenotype algorithms requires phenotype engineers to interpret human-readable algorithms and translate the description (text and flowcharts) into computable phenotypes – a process that can be labor intensive and error prone. To address the critical need for reducing the implementation efforts, it is important to develop portable algorithms. Methods We conducted a retrospective analysis of phenotype algorithms developed in the Electronic Medical Records and Genomics (eMERGE) network and identified common customization tasks required for implementation. A novel scoring system was developed to quantify portability from three aspects: Knowledge conversion, clause Interpretation, and Programming (KIP). Tasks were grouped into twenty representative categories. Experienced phenotype engineers were asked to estimate the average time spent on each category and evaluate time saving enabled by a common data model (CDM), specifically the Observational Medical Outcomes Partnership (OMOP) model, for each category. Results A total of 485 distinct clauses (phenotype criteria) were identified from 55 phenotype algorithms, corresponding to 1153 customization tasks. In addition to 25 non-phenotype-specific tasks, 46 tasks are related to interpretation, 613 tasks are related to knowledge conversion, and 469 tasks are related to programming. A score between 0 and 2 (0 for easy, 1 for moderate, and 2 for difficult portability) is assigned for each aspect, yielding a total KIP score range of 0 to 6. The average clause-wise KIP score to reflect portability is 1.37 ± 1.38. Specifically, the average knowledge (K) score is 0.64 ± 0.66, interpretation (I) score is 0.33 ± 0.55, and programming (P) score is 0.40 ± 0.64. 5% of the categories can be completed within one hour (median). 70% of the categories take from days to months to complete. The OMOP model can assist with vocabulary mapping tasks. Conclusion This study presents firsthand knowledge of the substantial implementation efforts in phenotyping and introduces a novel metric (KIP) to measure portability of phenotype algorithms for quantifying such efforts across the eMERGE Network. Phenotype developers are encouraged to analyze and optimize the portability in regards to knowledge, interpretation and programming. CDMs can be used to improve the portability for some ‘knowledge-oriented’ tasks.

Background Implementing clinical phenotypes across a network is labor intensive and potentially error prone. Use of a common data model may facilitate the process. Methods Electronic Medical Records and Genomics (eMERGE) sites implemented the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model across their electronic health record (EHR)-linked DNA biobanks. Two previously implemented eMERGE phenotypes were converted to OMOP and implemented across the network. Results It was feasible to implement the common data model across sites, with laboratory data producing the greatest challenge due to local encoding. Sites were then able to execute the OMOP phenotype in less than one day, as opposed to weeks of effort to manually implement an eMERGE phenotype in their bespoke research EHR databases. Of the sites that could compare the current OMOP phenotype implementation with the original eMERGE phenotype implementation, specific agreement ranged from 100% to 43%, with disagreements due to the original phenotype, the OMOP phenotype, changes in data, and issues in the databases. Using the OMOP query as a standard comparison revealed differences in the original implementations despite starting from the same definitions, code lists, flowcharts, and pseudocode. Conclusion Using a common data model can dramatically speed phenotype implementation at the cost of having to populate that data model, though this will produce a net benefit as the number of phenotype implementations increases. Inconsistencies among the implementations of the original queries point to a potential benefit of using a common data model so that actual phenotype code and logic can be shared, mitigating human error in reinterpretation of a narrative phenotype definition.

An important problem in public clouds is how to selectively share documents based on fine-grained attribute-based access control policies (acps). An approach is to encrypt documents satisfying different policies with different keys using a public key cryptosystem such as attribute-based encryption, and/or proxy re-encryption. However, such an approach has some weaknesses: it cannot efficiently handle adding/revoking users or identity attributes, and policy changes; it requires to keep multiple encrypted copies of the same documents; it incurs high computational costs. A direct application of a symmetric key cryptosystem, where users are grouped based on the policies they satisfy and unique keys are assigned to each group, also has similar weaknesses. We observe that, without utilizing public key cryptography and by allowing users to dynamically derive the symmetric keys at the time of decryption, one can address the above weaknesses. Based on this idea, we formalize a new key management scheme, called broadcast group key management (BGKM), and then give a secure construction of a BGKM scheme called ACV-BGKM. The idea is to give some secrets to users based on the identity attributes they have and later allow them to derive actual symmetric keys based on their secrets and some public information. A key advantage of the BGKM scheme is that adding users/revoking users or updating acps can be performed efficiently by updating only some public information. Using our BGKM construct, we propose an efficient approach for fine-grained encryption-based access control for documents stored in an untrusted cloud file storage.

International audience; Users hesitate to submit negative feedback in reputation systems due to the fear of retaliation from the recipient user. A privacy preserving reputation protocol protects users by hiding their individual feedback and revealing only the reputation score. We present a privacy preserving reputation protocol for the malicious adversarial model. The malicious users in this model actively attempt to learn the private feedback values of honest users as well as to disrupt the protocol. Our protocol does not require centralized entities, trusted third parties, or specialized platforms, such as anonymous networks and trusted hardware. Moreover, our protocol is efficient. It requires an exchange of O(n + log N) messages, where n and N are the number of users in the protocol and the environment respectively.

Objective: Harmonized data quality (DQ) assessment terms, methods, and reporting practices can establish a common understanding of the strengths and limitations of electronic health record (EHR) data for operational analytics, quality improvement, and research. Existing published DQ terms were harmonized to a comprehensive unified terminology with definitions and examples and organized into a conceptual framework to support a common approach to defining whether EHR data is ‘fit’ for specific uses.Materials and Methods: DQ publications, informatics and analytics experts, managers of established DQ programs, and operational manuals from several mature EHR-based research networks were reviewed to identify potential DQ terms and categories. Two face-to-face stakeholder meetings were used to vet an initial set of DQ terms and definitions that were grouped into an overall conceptual framework. Feedback received from data producers and users was used to construct a draft set of harmonized DQ terms and categories. Multiple rounds of iterative refinement resulted in a set of terms and organizing framework consisting of DQ categories, subcategories, terms, definitions, and examples. The harmonized terminology and logical framework’s inclusiveness was evaluated against ten published DQ terminologies.Results: Existing DQ terms were harmonized and organized into a framework by defining three DQ categories: (1) Conformance (2) Completeness and (3) Plausibility and two DQ assessment contexts: (1) Verification and (2) Validation. Conformance and Plausibility categories were further divided into subcategories. Each category and subcategory was defined with respect to whether the data may be verified with organizational data, or validated against an accepted gold standard, depending on proposed context and uses. The coverage of the harmonized DQ terminology was validated by successfully aligning to multiple published DQ terminologies.Discussion: Existing DQ concepts, community input, and expert review informed the development of a distinct set of terms, organized into categories and subcategories. The resulting DQ terms successfully encompassed a wide range of disparate DQ terminologies. Operational definitions were developed to provide guidance for implementing DQ assessment procedures. The resulting structure is an inclusive DQ framework for standardizing DQ assessment and reporting. While our analysis focused on the DQ issues often found in EHR data, the new terminology may be applicable to a wide range of electronic health data such as administrative, research, and patient-reported data.Conclusion: A consistent, common DQ terminology, organized into a logical framework, is an initial step in enabling data owners and users, patients, and policy makers to evaluate and communicate data quality findings in a well-defined manner with a shared vocabulary. Future work will leverage the framework and terminology to develop reusable data quality assessment and reporting methods.

In electronic subscription and pay TV systems, data can be organized and encrypted using symmetric key algorithms according to predefined time periods and user privileges and then broadcast to users. This requires an efficient way of managing the encryption keys. In this scenario, time-bound key management schemes for a hierarchy were proposed by Tzeng and Chien in 2002 and 2005, respectively. Both schemes are insecure against collusion attacks. In this paper, we propose a new key assignment scheme for access control, which is both efficient and secure. Elliptic-curve cryptography is deployed in this scheme. We also provide the analysis of the scheme with respect to security and efficiency issues.

The ability to seamlessly scale on demand has made Content-Based Publish-Subscribe (CBPS) systems the choice of distributing messages/documents produced by Content Publishers to many Subscribers through Content Brokers. Most of the current systems assume that Content Brokers are trusted for the confidentiality of the data published by Content Publishers and the privacy of the subscriptions, which specify their interests, made by Subscribers. However, with the increased use of technologies, such as service oriented architectures and cloud computing, essentially outsourcing the broker functionality to third-party providers, one can no longer assume the trust relationship to hold. The problem of providing privacy/confidentiality in CBPS systems is challenging, since the solution to the problem should allow Content Brokers to make routing decisions based on the content without revealing the content to them. The previous work attempted to solve this problem was not fully successful. The problem may appear unsolvable since it involves conflicting goals, but in this paper, we propose a novel approach to preserve the privacy of the subscriptions made by Subscribers and confidentiality of the data published by Content Publishers using cryptographic techniques when third-party Content Brokers are utilized to make routing decisions based on the content. Our protocols are expressive to support any type of subscriptions and designed to work efficiently. We distribute the work such that the load on Content Brokers, where the bottleneck is in a CBPS system, is minimized. We extend a popular CBPS system using our protocols to implement a privacy preserving CBPS system.

We propose to demonstrate Mask, the first system addressing the seemingly-unsolvable problem of how to selectively share contents among a group of users based on access control policies expressed as conditions against the identity attributes of these users while at the same time assuring the privacy of these identity attributes from the content publisher. Mask consists of three entities: a Content Publisher, Users referred to as Subscribers, and Identity Providers that issue certified identity attributes. The content publisher specifies access control policies against identity attributes of subscribers indicating which conditions the identity attributes of a subscriber must verify in order for this subscriber to access a document or a subdocument. The main novelty of Mask is that, even though the publisher is able to match the identity attributes of the subscribers against its own access control policies, the publisher does not learn the values of the identity attributes of the subscribers; the privacy of the authorized subscribers is thus preserved. Based on the specified access control policies, documents are divided into subdocuments and the subdocuments having different access control policies are encrypted with different keys. Subscribers derive the keys corresponding to the subdocuments they are authorized to access. Key distribution in Mask is supported by a novel group key management protocol by which subscribers can reconstruct the decryption keys from the subscription information they receive from the publisher. The publisher however does not learn which decryption keys each subscriber is able to reconstruct. In this demonstration, we show our system using a healthcare scenario.

Modern access control models, developed for protecting data from accesses across the Internet, require to verify the identity of users in order to make sure that users have the required permissions for accessing the data. User's identity consists of data, referred to as identity attributes, that encode relevant-security properties of the users. Because identity attributes often convey sensitive information about users, they have to be protected. The Oblivious Commitment-Based Envelope (OCBE) protocols address the protection requirements of both users and service providers. The OCBE protocols makes it possible for a party, referred as sender, to send an encrypted message to a receiver such that the receiver can open the message if and only if its committed value satisfies a predicate and that the sender does not learn anything about the receiver's committed value. The possible predicates are comparison predicates =, ≠, >

Private Information Retrieval (PIR) protocols allow users to learn data items stored at a server which is not fully trusted, without disclosing to the server the particular data element retrieved. Several PIR protocols have been proposed, which provide strong guarantees on user privacy. Nevertheless, in many application scenarios it is important to protect the database as well. In this paper, we investigate the amount of data disclosed by the the most prominent PIR protocols during a single run. We show that a malicious user can stage attacks that allow an excessive amount of data to be retrieved from the server. Furthermore, this vulnerability can be exploited even if the client follows the legitimate steps of the PIR protocol, hence the malicious request can not be detected and rejected by the server. We devise mechanisms that limit the PIR disclosure to a single data item.

We propose a novel scheme for selective distribution of content, encoded as documents, that preserves the privacy of the users to whom the documents are delivered and is based on an efficient and novel group key management scheme. Our document broadcasting approach is based on access control policies specifying which users can access which documents, or subdocuments. Based on such policies, a broadcast document is segmented into multiple subdocuments, each encrypted with a different key. In line with modern attribute-based access control, policies are specified against identity attributes of users. However our broadcasting approach is privacy-preserving in that users are granted access to a specific document, or subdocument, according to the policies without the need of providing in clear information about their identity attributes to the document publisher. Under our approach, not only does the document publisher not learn the values of the identity attributes of users, but it also does not learn which policy conditions are verified by which users, thus inferences about the values of identity attributes are prevented. Moreover, our key management scheme on which the proposed broadcasting approach is based is efficient in that it does not require to send the decryption keys to the users along with the encrypted document. Users are able to reconstruct the keys to decrypt the authorized portions of a document based on subscription information they have received from the document publisher. The scheme also efficiently handles new subscription of users and revocation of subscriptions.

The combined use of the Internet and mobile technologies is leading to major changes in how individuals communicate, conduct business transactions and access resources and services. In such a scenario, digital identity management (DIM) technology is fundamental for enabling transactions and interactions across the Internet. In this demo, we demonstrate VeryIDX, a system for the privacy-preserving management of users' identity attributes on mobile devices.

Workflow is referred to automatic business process, and current involving methods mainly depend on traditional centralized C/S infrastructure and email systems. As far as we know, the existing methods have following weaknesses in nature: 1) The abilities of workflow processing is difficult to scale up; 2) Workflow execution is not flexible; 3) Workflow transfer process is not reliable; 4) Handheld devices in mobile environments are seldom considered. However, peer-to-peer technology is characterized by decentralized control, large scale, and extreme dynamism, while agent technology can construct overlay network more easily and process workflow automatically. In this paper, we present a novel framework based on office-secretary model, which utilizes concept of peer-to-peer, agent and transaction mechanism. In particular, we believe that iOmS framework could easily support scalable, dynamic, reliable, and ubiquitous workflow applications.