Abstract
BackgroundRecord linkage of existing individual health care data is an efficient way to answer important epidemiological research questions. Reuse of individual health-related data faces several problems: Either a unique personal identifier, like social security number, is not available or non-unique person identifiable information, like names, are privacy protected and cannot be accessed. A solution to protect privacy in probabilistic record linkages is to encrypt these sensitive information. Unfortunately, encrypted hash codes of two names differ completely if the plain names differ only by a single character. Therefore, standard encryption methods cannot be applied. To overcome these challenges, we developed the Privacy Preserving Probabilistic Record Linkage (P3RL) method.MethodsIn this Privacy Preserving Probabilistic Record Linkage method we apply a three-party protocol, with two sites collecting individual data and an independent trusted linkage center as the third partner. Our method consists of three main steps: pre-processing, encryption and probabilistic record linkage. Data pre-processing and encryption are done at the sites by local personnel. To guarantee similar quality and format of variables and identical encryption procedure at each site, the linkage center generates semi-automated pre-processing and encryption templates. To retrieve information (i.e. data structure) for the creation of templates without ever accessing plain person identifiable information, we introduced a novel method of data masking. Sensitive string variables are encrypted using Bloom filters, which enables calculation of similarity coefficients. For date variables, we developed special encryption procedures to handle the most common date errors. The linkage center performs probabilistic record linkage with encrypted person identifiable information and plain non-sensitive variables.ResultsIn this paper we describe step by step how to link existing health-related data using encryption methods to preserve privacy of persons in the study.ConclusionPrivacy Preserving Probabilistic Record linkage expands record linkage facilities in settings where a unique identifier is unavailable and/or regulations restrict access to the non-unique person identifiable information needed to link existing health-related data sets. Automated pre-processing and encryption fully protect sensitive information ensuring participant confidentiality. This method is suitable not just for epidemiological research but also for any setting with similar challenges.
Highlights
Record linkage of existing individual health care data is an efficient way to answer important epidemiological research questions
A major challenge for data reuse is that regulations commonly prohibit disclosure of discriminating personal identifying information ([Personal Identifying Information (PII)], e.g. name, address, social security number [Social Security Number (SSN)], date of birth [Date of Birth (DOB)])
In our work we have found that 17-19 % of all errors in linked DOB are due to ±1 day differences
Summary
Record linkage of existing individual health care data is an efficient way to answer important epidemiological research questions. Reuse of individual health-related data faces several problems: Either a unique personal identifier, like social security number, is not available or non-unique person identifiable information, like names, are privacy protected and cannot be accessed. Record linkage of existing individual health-related data is a time and cost efficient way to answer important epidemiologic research questions. Older Swiss datasets include nonunique PII and newer datasets have SSN, neither can be used across settings due to legal restrictions This creates an intractable challenge for researchers who want to take advantage of the efficiencies of reusing existing health-related data [3, 4]
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