Data related to households or addresses needs be published in an aggregated form to obfuscate sensitive information about individuals. Usually, the data is aggregated to the level of existing administrative zones, but these often do not correspond to formal models of privacy or a desired level of anonymity. Therefore, automatic privacy-preserving spatial clustering methods are needed. To address this need, we present algorithms to partition a given set of locations into k-anonymous clusters, meaning that each cluster contains at least k locations. We assume that the locations are given as a set T ⊆ V of terminals in a weighted graph G = (V, E) representing a road network. Our approach is to compute a forest in G, i.e., a set of trees, each of which corresponds to a cluster. We ensure the k-anonymity of the clusters by constraining the trees to span at leastterminals each (plus an arbitrary number of non-terminal nodes called Steiner nodes). By minimizing the total edge weight of the forest, we ensure that the clusters reflect the proximity among the locations. Although the problem is NP-hard, we were able to solve instances of several hundreds of terminals using integer linear programming. Moreover, we present an efficient approximation algorithm and show that it can be used to process large and fine-grained data sets.

In the United States, only about 10 percent of people with an alcohol or drug use disorder receive care for the condition, pointing to a large treatment gap. Several personal characteristics influence whether a person will receive treatment; additionally, many people with an alcohol use disorder do not perceive the need for treatment. The extent of the treatment gap differs somewhat across different population subgroups, such as those based on gender, age, or race and ethnicity. Recent health care reforms, such as implementation of the Patient Protection and Affordable Care Act of 2010, likely will improve access to substance abuse treatment. In addition, new treatment approaches, service delivery systems, and payment innovations may facilitate access to substance abuse services. Nevertheless, efforts to bridge the treatment gap will continue to be needed to ensure that all people who need alcohol and drug abuse treatment can actually receive it.