This paper introduces a new multidimensional k-anonymity model that provides greater flexibility compared to previous single-dimensional approaches. The model allows for more effective anonymizations, as measured by both general-purpose metrics and specific query answerability. The paper proves that optimal multidimensional k-anonymization is NP-hard, but proposes a scalable, greedy algorithm that produces a constant-factor approximation of the optimal solution. Experimental results show that this greedy algorithm often produces better results than optimal single-dimensional algorithms. The paper defines key concepts such as quasi-identifier attributes, equivalence classes, and k-anonymity. It also introduces general-purpose quality metrics, including the discernability metric and the normalized average equivalence class size metric. The paper then presents a new multidimensional partitioning model for k-anonymization, which is proven to be NP-hard. The paper also introduces a greedy algorithm for multidimensional k-anonymization, which is scalable and produces a constant-factor approximation of the optimal solution. The paper discusses the differences between single-dimensional and multidimensional partitioning models, and shows that the optimal strict multidimensional partitioning problem is NP-hard. The paper also presents worst-case bounds on the size of regions in a minimal multidimensional partitioning, and shows that the maximum size of a region resulting from a minimal multidimensional partitioning is O(k) for a constant-sized quasi-identifier. The paper also discusses the differences between single-dimensional and multidimensional partitioning models, and shows that the optimal strict multidimensional partitioning problem is NP-hard. The paper also presents worst-case bounds on the size of regions in a minimal multidimensional partitioning, and shows that the maximum size of a region resulting from a minimal multidimensional partitioning is O(k) for a constant-sized quasi-identifier. The paper introduces a greedy algorithm for multidimensional k-anonymization, which is scalable and produces a constant-factor approximation of the optimal solution. The paper also discusses the scalability of the algorithm, and shows that it can be adapted to either strict or relaxed partitioning models. The paper also discusses the bounds on quality metrics for both strict and relaxed partitioning models, and shows that the greedy algorithm produces a constant-factor approximation of the optimal solution. The paper also discusses workload-driven quality measurement, which considers the anticipated workload when designing an anonymization scheme. The paper introduces the concept of predicate-range matching, which is used to evaluate the accuracy of aggregate queries on anonymized data. The paper also discusses the effects of multidimensional versus single-dimensional recoding on query processing, and shows that multidimensional recoding can lead to more accurate query results. The paper presents experimental results comparing the quality of anonymizations produced by the greedy algorithm with those produced by optimal algorithms for two other models: full-domain generalization and single-dimensional partitioning. The results show that the greedy algorithm often produces better results than the optimal algorithms. The paper also discusses the scalability of the algorithm, and shows that it can be adapted to either strict or relaxed partitioning modelsThis paper introduces a new multidimensional k-anonymity model that provides greater flexibility compared to previous single-dimensional approaches. The model allows for more effective anonymizations, as measured by both general-purpose metrics and specific query answerability. The paper proves that optimal multidimensional k-anonymization is NP-hard, but proposes a scalable, greedy algorithm that produces a constant-factor approximation of the optimal solution. Experimental results show that this greedy algorithm often produces better results than optimal single-dimensional algorithms. The paper defines key concepts such as quasi-identifier attributes, equivalence classes, and k-anonymity. It also introduces general-purpose quality metrics, including the discernability metric and the normalized average equivalence class size metric. The paper then presents a new multidimensional partitioning model for k-anonymization, which is proven to be NP-hard. The paper also introduces a greedy algorithm for multidimensional k-anonymization, which is scalable and produces a constant-factor approximation of the optimal solution. The paper discusses the differences between single-dimensional and multidimensional partitioning models, and shows that the optimal strict multidimensional partitioning problem is NP-hard. The paper also presents worst-case bounds on the size of regions in a minimal multidimensional partitioning, and shows that the maximum size of a region resulting from a minimal multidimensional partitioning is O(k) for a constant-sized quasi-identifier. The paper also discusses the differences between single-dimensional and multidimensional partitioning models, and shows that the optimal strict multidimensional partitioning problem is NP-hard. The paper also presents worst-case bounds on the size of regions in a minimal multidimensional partitioning, and shows that the maximum size of a region resulting from a minimal multidimensional partitioning is O(k) for a constant-sized quasi-identifier. The paper introduces a greedy algorithm for multidimensional k-anonymization, which is scalable and produces a constant-factor approximation of the optimal solution. The paper also discusses the scalability of the algorithm, and shows that it can be adapted to either strict or relaxed partitioning models. The paper also discusses the bounds on quality metrics for both strict and relaxed partitioning models, and shows that the greedy algorithm produces a constant-factor approximation of the optimal solution. The paper also discusses workload-driven quality measurement, which considers the anticipated workload when designing an anonymization scheme. The paper introduces the concept of predicate-range matching, which is used to evaluate the accuracy of aggregate queries on anonymized data. The paper also discusses the effects of multidimensional versus single-dimensional recoding on query processing, and shows that multidimensional recoding can lead to more accurate query results. The paper presents experimental results comparing the quality of anonymizations produced by the greedy algorithm with those produced by optimal algorithms for two other models: full-domain generalization and single-dimensional partitioning. The results show that the greedy algorithm often produces better results than the optimal algorithms. The paper also discusses the scalability of the algorithm, and shows that it can be adapted to either strict or relaxed partitioning models