linked records would be correct (since two identifiers already agree), but would potentially miss records referring to the same person whose surname or ZIP code was different (due to marriage or relocation, for instance). Blocking based on birth month, a more stable identifier that would be expected to change only in the case of data error, would provide a more modest gain in positive predictive value and loss in sensitivity, but would create only twelve distinct groups which, for extremely large data sets, may not provide much net improvement in computation speed. Thus, robust record linkage systems often use multiple blocking passes to group data in various ways in order to come up with groups of records that should be compared to each other. Machine learning In recent years, a variety of machine learning techniques have been used in record linkage. It has been recognized that the classic Fellegi-Sunter algorithm for probabilistic record linkage outlined above is equivalent to the Naive Bayes algorithm in the field of machine learning, and suffers from the same assumption of the independence of its features (an assumption that is typically not true). Higher accuracy can often be achieved by using various other machine learning techniques, including a single-layer perceptron, random forest, and SVM. In conjunction with distributed technologies, accuracy and scale for record linkage can be improved further. Human-machine hybrid record linkage High quality record linkage often requires a human–machine hybrid system to safely manage uncertainty in the ever changing streams of chaotic big data. Recognizing that linkage errors propagate into the linked data and its analysis, interactive record linkage systems have been proposed. Interactive record linkage is defined as people iteratively fine tuning the results from the automated methods and managing the uncertainty and its propagation to subsequent analyses. The main objectives of interactive record linkage systems is to manually resolve uncertain linkages and validate the results until it is at acceptable levels for the given application. Variations of interactive record linkage that enhance privacy during the human interaction steps have also been proposed. Privacy-preserving record linkage Record linkage is increasingly required across databases held by different organisations, where the complementary data held by these organisations can, for example, help to identify patients that are susceptible to certain adverse drug reactions (linking hospital, doctor, pharmacy databases). In many such applications, however, the databases to be linked contain sensitive information about people which cannot