With the proliferation of wireless sensor net- works and mobile technologies in general, it is possible to provide improved medical services and also to reduce costs as well as to manage the shortage of specialized personnel. Monitoring a person’s health condition using sensors pro- vides a lot of benefits but also exposes personal sensitive information to a number of privacy threats. By recording user-related data, it is often feasible for a malicious or negligent data provider to expose these data to an unau- thorized user. One solution is to protect the patient’s pri- vacy by making difficult a linkage between specific measurements with a patient’s identity. In this paper we present a privacy-preserving architecture which builds upon the concept of k -anonymity; we present a clustering- based anonymity scheme for effective network manage- ment and data aggregation, which also protects user’s privacy by making an entity indistinguishable from other k similar entities. The presented algorithm is resource aware, as it minimizes energy consumption with respect to other more costly, cryptography-based approaches. The system is evaluated from an energy-consuming and net- work performance perspective, under different simulation scenarios.