Establishing a Framework for Privacy-Preserving Record Linkage among Electronic Health Record and Administrative Claims Databases within PCORnet®, the National Patient-Centered Clinical Research Network
Daniel KiernanThomas W. CartonSengwee TohJasmin PhuaMaryan ZirkleDarcy LouzaoKevin HaynesMark G. WeinerFrancisco AnguloCharles BaileyJiang BianDaniel FortShaun J. GrannisAshok KrishnamurthyVinit NairPedro RiveraJonathan C. SilversteinKeith Marsolo
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Abstract Objective: The aim of this study was to determine whether a secure, privacy-preserving record linkage (PPRL) methodology can be implemented in a scalable manner for use in a large national clinical research network. Results: We established the governance and technical capacity to support the use of PPRL across the National Patient-Centered Clinical Research Network (PCORnet ® ). As a pilot, four sites used the Datavant software to transform patient personally identifiable information (PII) into de-identified tokens. We queried the sites for patients with a clinical encounter in 2018 or 2019 and matched their tokens to determine whether overlap existed. We described patient overlap among the sites and generated a “deduplicated” table of patient demographic characteristics. Overlapping patients were found in 3 of the 6 site-pairs. Following deduplication, the total patient count was 3,108,515 (0.11% reduction), with the largest reduction in count for patients with an “Other/Missing” value for Sex; from 198 to 163 (17.6% reduction). The PPRL solution successfully links patients across data sources using distributed queries without directly accessing patient PII. The overlap queries and analysis performed in this pilot is being replicated across the full network to provide additional insight into patient linkages among a distributed research network.Keywords:
Linkage (software)
Data deduplication
Medical record
Table (database)
Deduplication, a form of compression aiming to eliminate duplicates in data, has become an important feature of most commercial and research backup systems. Since the advent of deduplication, most research efforts have focused on maximizing deduplication efficiency--i.e., the offered compression ratio--and have achieved near-optimal usage of raw storage. However, the capacity goals of next-generation Petabyte systems requires a highly scalable design, able to overcome the current scalability limitations of deduplication. We advocate a shift towards scalability-centric design principles for deduplication systems, and present some of the mechanisms used in our prototype, aiming at high scalability, good deduplication efficiency, and high throughput.
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Record linkage is aimed at the accurate and efficient identification of records that represent the same entity within or across disparate databases. It is a fundamental task in data integration and increasingly required for accurate decision making in application domains ranging from health analytics to national security. Traditional record linkage techniques calculate string similarities between quasi-identifying (QID) values, such as the names and addresses of people. Errors, variations, and missing QID values can however lead to low linkage quality because the similarities between records cannot be calculated accurately. To overcome this challenge, we propose a novel technique that can accurately link records even when QID values contain errors or variations, or are missing. We first generate attribute signatures (concatenated QID values) using an Apriori based selection of suitable QID attributes, and then relational signatures that encapsulate relationship information between records. Combined, these signatures can uniquely identify individual records and facilitate fast and high quality linking of very large databases through accurate similarity calculations between records. We evaluate the linkage quality and scalability of our approach using large real-world databases, showing that it can achieve high linkage quality even when the databases being linked contain substantial amounts of missing values and errors.
Record Linkage
Linkage (software)
Similarity (geometry)
Data deduplication
Identification
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Cloud computing provides scalable, low-cost and location-independent services over the internet. The services provided ranges from simple backup services to cloud storage infrastructures. The fast growth of data volumes has greatly increased the demand for techniques for saving disk space and network bandwidth. Cloud storage services like Dropbox, Mozy, Google Drive choose a deduplication technique where the cloud server stores only a single copy of redundant data and creates links to the copy instead of storing actual copies. The security of users data become a new challenge. Hence the users encrypt the data before outsourcing to the cloud. Conventional encryption techniques are incompatible with deduplication while convergent encryption resolves this problem effectively. Various research papers have been studied from the literature, as a result, this paper attempts to survey data deduplication techniques in cloud storage along with concepts, categories and methods used in data deduplication.
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Cloud storage
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Data deduplication
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The cloud storage services are used to store intermediate and persistent data generated from various resources including servers and IoT based networks.The outcome of such developments is that the data gets duplicated and gets replicated rapidly especially when large number of cloud users are working in a collaborative environment to solve large scale problems in geo-distributed networks.The data gets prone to breach of privacy and high incidence of duplication.When the dynamics of cloud services change over period of time, the ownership and proof of identity operations also need to change and work dynamically for high degree of security.In this work we will study the following concepts, methods and the schemes that can make the cloud services secure and reduce the incidence of data duplication.With the help of cryptography mathematics and to increase potential storage capacity.The proposed scheme works for deduplication of data with arithmetic key validity operations that reduce the overhead and increase the complexity of the keys so that it is hard to break the keys.
Data deduplication
Cloud storage
Homomorphic Encryption
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The presence of duplicate records is a major data quality concern in large databases. To detect duplicates, entity resolution also known as duplication detection or record linkage is used as a part of the data cleaning process to identify records that potentially refer to the same real-world entity. We present the Stringer system that provides an evaluation framework for understanding what barriers remain towards the goal of truly scalable and general purpose duplication detection algorithms. In this paper, we use Stringer to evaluate the quality of the clusters (groups of potential duplicates) obtained from several unconstrained clustering algorithms used in concert with approximate join techniques. Our work is motivated by the recent significant advancements that have made approximate join algorithms highly scalable. Our extensive evaluation reveals that some clustering algorithms that have never been considered for duplicate detection, perform extremely well in terms of both accuracy and scalability.
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Бұл зерттеужұмысындaКaно моделітурaлы жәнеоғaн қaтыстытолықмәліметберілгенжәнеуниверситетстуденттерінебaғыттaлғaн қолдaнбaлы (кейстік)зерттеужүргізілген.АхметЯссaуи университетініңстуденттеріүшін Кaно моделіқолдaнылғaн, олaрдың жоғaры білімберусaпaсынa қоятынмaңыздытaлaптaры, яғнисaпaлық қaжеттіліктері,олaрдың мaңыздылығытурaлы жәнесaпaлық қaжеттіліктерінеқaтыстыөз университетінқaлaй бaғaлaйтындығытурaлы сұрaқтaр қойылғaн. Осы зерттеудіңмaқсaты АхметЯсaуи университетіндетуризмменеджментіжәнеқaржы бaкaлaвриaт бaғдaрлaмaлaрыныңсaпaсынa қaтыстыстуденттердіңқaжеттіліктерінaнықтaу, студенттердіңқaнaғaттaну, қaнaғaттaнбaу дәрежелерінбелгілеу,білімберусaпaсын aнықтaу мен жетілдіружолдaрын тaлдaу болыптaбылaды. Осы мaқсaтқaжетуүшін, ең aлдыменКaно сaуaлнaмaсы түзіліп,116 студенткеқолдaнылдыжәнебілімберугежәнеоның сaпaсынa қaтыстыстуденттердіңтaлaптaры мен қaжеттіліктерітоптықжұмыстaрaрқылыaнықтaлды. Екіншіден,бұл aнықтaлғaн тaлaптaр мен қaжеттіліктерКaно бaғaлaу кестесіменжіктелді.Осылaйшa, сaпa тaлaптaры төрт сaнaтқa бөлінді:болуытиіс, бір өлшемді,тaртымдыжәнебейтaрaп.Соңындa,қaнaғaттaну мен қaнaғaттaнбaудың мәндеріесептелдіжәнестуденттердіңқaнaғaттaну мен қaнaғaттaнбaу деңгейлерінжоғaрылaту мен төмендетудеосытaлaптaр мен қaжеттіліктердіңрөліaйқын aнықтaлды.Түйінсөздер:сaпa, сaпaлық қaжеттіліктер,білімберусaпaсы, Кaно моделі.
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The nationally-recognized Susquehanna
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