1. A computerized method for anonymizing a dataset that associates each entity included in a set of entities with a set of two or more attribute values, wherein each said attribute value associated with the entity is associated with a different entity attribute, the method being performed by a computer system and comprising: receiving preference information that comprises a set of attribute weights, wherein each attribute weight included in the set is associated with one of the entity attributes; for each entity included in the set of entities, transforming said set of two or more attribute values associated with the entity using the preference information, thereby creating for the entity a set of two or more transformed attribute values, wherein the transforming the set of two or more attribute values comprises: normalizing the set of two or more attribute values to produce normalized attribute values, and after normalizing the set of two or more attribute values to produce normalized attribute values, calculating, for each of the normalized attribute values, a transformed attribute value using the normalized attribute value and the attribute weight associated with the entity attribute with which the normalized attribute value is associated; clustering the entities included in the set of entities using said transfoinied attribute values to form at least a first entity cluster consisting of a first subset of the entities and a second entity cluster consisting of a second subset of the entities, wherein no entity included in the first entity cluster is included in the second entity cluster; anonymizing the first subset of entities; and anonymizing the second subset of entities.
1. A computerized method for anonymizing a dataset that associates each entity included in a set of entities with a set of two or more attribute values, wherein each said attribute value associated with the entity is associated with a different entity attribute, the method being performed by a computer system and comprising: receiving preference information that comprises a set of attribute weights, wherein each attribute weight included in the set is associated with one of the entity attributes; for each entity included in the set of entities, transforming said set of two or more attribute values associated with the entity using the preference information, thereby creating for the entity a set of two or more transformed attribute values, wherein the transforming the set of two or more attribute values comprises: normalizing the set of two or more attribute values to produce normalized attribute values, and after normalizing the set of two or more attribute values to produce normalized attribute values, calculating, for each of the normalized attribute values, a transformed attribute value using the normalized attribute value and the attribute weight associated with the entity attribute with which the normalized attribute value is associated; clustering the entities included in the set of entities using said transfoinied attribute values to form at least a first entity cluster consisting of a first subset of the entities and a second entity cluster consisting of a second subset of the entities, wherein no entity included in the first entity cluster is included in the second entity cluster; anonymizing the first subset of entities; and anonymizing the second subset of entities. 8. The computerized method of claim 1 , wherein clustering the entities comprises partitioning the entities in the set of entities into clusters each having entities with similar transformed attribute values.