Minkowski's convex body theorem and integer programming
Mathematics of Operations Research
Fixed-parameter tractability and completeness II: on completeness for W[1]
Theoretical Computer Science
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Generalizing data to provide anonymity when disclosing information (abstract)
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Some APX-completeness results for cubic graphs
Theoretical Computer Science
Approximation algorithms
Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Algorithmic construction of sets for k-restrictions
ACM Transactions on Algorithms (TALG)
Approximate algorithms for K-anonymity
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
k-Anonymization with Minimal Loss of Information
IEEE Transactions on Knowledge and Data Engineering
The hardness and approximation algorithms for l-diversity
Proceedings of the 13th International Conference on Extending Database Technology
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming
Resolving the complexity of some data privacy problems
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming: Part II
Parameterized complexity of k-anonymity: hardness and tractability
IWOCA'10 Proceedings of the 21st international conference on Combinatorial algorithms
Anonymizing binary and small tables is hard to approximate
Journal of Combinatorial Optimization
Pattern-guided data anonymization and clustering
MFCS'11 Proceedings of the 36th international conference on Mathematical foundations of computer science
On the complexity of the l-diversity problem
MFCS'11 Proceedings of the 36th international conference on Mathematical foundations of computer science
The effect of homogeneity on the complexity of k-anonymity
FCT'11 Proceedings of the 18th international conference on Fundamentals of computation theory
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
ICDT'05 Proceedings of the 10th international conference on Database Theory
The effect of homogeneity on the computational complexity of combinatorial data anonymization
Data Mining and Knowledge Discovery
| Hi-index | 5.23 |
Publishing personal data without giving up privacy is becoming an increasingly important problem in different fields. In the last years, different interesting approaches have been proposed, i.e. k-Anonymity and l-Diversity. Given an input table, these approaches partition its rows so that the computed partition satisfies some constraint, in order to prevent the inference of the individuals the data belong to. Then, the rows in a same set of the partition are related to the same rows by suppressing some of their entries. Here we focus on the l-Diversity problem, where the attributes of the input table are distinguished in sensitive attributes and quasi-identifier attributes. The goal is to partition the rows of the input table, so that each set C of the partition contains at most 1l|C| rows having a specific value in the sensitive attribute, and the number of suppressions is minimized. In this paper we investigate the approximation and parameterized complexity ofl-Diversity. First, we prove that the problem is not approximable within factor clnl, for some constant c0, even if the input table consists of only two columns, and that the problem is APX-hard, even if l=4 and the input table contains exactly three columns. Then we give an approximation algorithm of factor m (where m+1 is the number of columns in the input table), when the sensitive attribute ranges over an alphabet of constant size. Concerning the parameterized complexity, we prove that the problem is W[1]-hard when parameterized by the cost-bound, by l, and by the size of the alphabet. Then we prove that the problem admits a fixed-parameter algorithm when both the maximum number of different values in a column and the number of columns are parameters.