Today's globally networked society places great demand on the collection and sharing of person-specific data for many new uses. Monitoring personal locations with a potentially untrusted server poses privacy threats to the monitored individuals. This paper provides a formal presentation of combining generalization and suppression to achieve k-anonymity. Generalization involves replacing a value with a less specific but semantically consistent value. Suppression involves not releasing a value at all. The Preferred Minimal Generalization Algorithm (MinGen), which is a theoretical algorithm presented herein, combines these techniques to provide k-anonymity protection with minimal distortion.