Jul 04, 2020

CiteSeerX — Review: l-diversity – privacy beyond k-anonymity CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract. This survey intends to summarize the paper [MaGK06] with a critical point of view. The paper deals with possibilities of attacking the k-anonymity generalization method and provides a method to circumvent potential problems. In this survey, we present the fundamental ideas of the paper and its main l diversity k anonymity for privacy preserving data ( Java Jun 26, 2014

Achieving Privacy Preservation Constraints in Missing

On privacy, research, and privacy research. l-diversity, because reidentification doesn't tell the whole story 2018-02-19 Where does privacy risk come from, when releasing anonymized data? What exactly can go wrong? Sweeney provided the first obvious answer: privacy risk appears when you can reidentify a … k-anonymity and l-diversity | History of an Idea: Missing Data

Improved l-diversity: Scalable anonymization approach for

Apart from this minor change, the only difference with the original Incognito algorithm is the different privacy definition (i.e., various l-diversity instantiations). Field Summary; private LatticeManager: man Lattice manager that controls how the generalization lattice is traversed . Welcome | 2017 IEEE International Conference on Data The annual IEEE International Conference on Data Engineering (ICDE) addresses research issues in designing, building, managing, and evaluating advanced data-intensive systems and applications. Daniel Kifer - Penn State Research Interests Literally everything. My recent projects include statistical privacy (e.g., differential privacy) and its interactions with software security, statistical inference, and numerical optimization. I have also been exploring deep learning algorithms and applications to the physical sciences as well as image processing. I have a particular interest in working with social science "Privacy Preserving User Data Publication In Social To achieve such privacy, we propose various schemes that combine the privacy of all the elements of a social network: node, edge, and attribute privacy by clustering the users based on their attribute similarity. We combine the concepts of k-anonymity and l-diversity to achieve user privacy.