Many services and applications require users to provide a certain amount of information about themselves in order to receive an acceptable quality of service (QoS). Exemplary areas of use are location based services like route planning or the reporting of security incidents for critical infrastructure. Users putting emphasis on their privacy, for example through anonymization, therefore usually suffer from a loss of QoS. Some services however, may not even be feasible above a certain threshold of anonymization, resulting in unacceptable service quality. Hence, there need to be restrictions on the applied level of anonymization. To prevent the QoS from dropping below an unacceptable threshold, we introduce the concept of Bounded Privacy, a generic model to describe situations in which the achievable level of privacy is bounded by its relation to the service quality. We furthermore propose an approach to derive the optimal level of privacy for both discrete and continuous data.