Data aggregation is a central principle underlying many applications in computer science, from artificial intelligence to data security and privacy. Microaggregation is a special clustering problem where the goal is to cluster a set of points into groups of at least k points in such a way that groups are as homogeneous as possible. A usual homogeneity criterion is the minimization of the within-groups sum of squares. Microaggregation appeared in connection with anonymization of statistical databases. When discussing microaggregation for information systems, points are database records. This paper extends the use of microaggregation for k-anonymity to implement the recent property of p-sensitive k-anonymity in a more unified and less disruptive way. Then location privacy is investigated: two enhanced protocols based on a trusted-third party (TTP) are proposed and thereafter microaggregation is used to design a new TTP-free protocol for location privacy.