Several methods for providing edge and node-differential privacy for graphs have been devised. However, most of them publish graph statistics, not the edge-set of the randomized graph. We present a method for graph randomization that provides randomized response and allows for publishing differentially private graphs. We show that this method can be applied to sanitize data to train collaborative filtering algorithms for recommender systems. Our results afford plausible deniability to users in relation to their interests, with a controlled probability predefined by the user or the data controller. We show in an experiment with Facebook Likes data and psychodemographic profiles, that the accuracy of the profiling algorithms is preserved even when they are trained with differentially private data. Finally, we define privacy metrics to compare our method for different parameters of e with a k-anonymization method on the MovieLens dataset for movie recommendations. ; CC BY-NC-ND 4.0 This work was partially supported by the Swedish Research Council (Vetenskapsrådet) project DRIAT (VR 2016-03346), the Spanish Government under grants RTI2018-095094-B-C22 "CONSENT", and the UOC postdoctoral fellowship program. ICETE: International Conference on E-Business and Telecommunication Networks
Different types of data privacy techniques have been applied to graphs and social networks. They have been used under different assumptions on intruders' knowledge. i.e., different assumptions on what can lead to disclosure. The analysis of different methods is also led by how data protection techniques influence the analysis of the data. i.e., information loss or data utility. One of the techniques proposed for graph is graph perturbation. Several algorithms have been proposed for this purpose. They proceed adding or removing edges, although some also consider adding and removing nodes. In this paper we propose the study of these graph perturbation techniques from a different perspective. Following the model of standard database perturbation as noise addition, we propose to study graph perturbation as noise graph addition. We think that changing the perspective of graph sanitization in this direction will permit to study the properties of perturbed graphs in a more systematic way. ; CC BY 4.0 Also part of the Security and Cryptology book sub series (LNSC, volume 11737) This work was partially supported by the Swedish Research Council (Vetenskapsrådet) project DRIAT (VR 2016-03346), the Spanish Government under grants RTI2018-095094-B-C22 "CONSENT" and TIN2014-57364-C2-2-R "SMARTGLACIS", and the UOC postdoctoral fellowship program.
In this work we present an algorithm for k-anonymization of datasets that are changing over time. It is intended for preventing identity disclosure in dynamic datasets via microaggregation. It supports adding, deleting and updating records in a database, while keeping k-anonymity on each release. We carry out experiments on database anonymization. We expected that the additional constraints for k-anonymization of dynamic databases would entail a larger information loss, however it stays close to MDAV's information loss for static databases. Finally, we carry out a proof of concept experiment with directed degree sequence anonymization, in which the removal or addition of records, implies the modification of other records. ; CC BY 4.0 Also part of the Security and Cryptology book sub series (LNSC, volume 11025) Julián Salas acknowledges the support of a UOC postdoctoral fellowship. This work is partly funded by the Spanish Government through grant TIN2014-57364-C2-2-R "SMARTGLACIS". Vicenç Torra acknowledges the suport of Vetenskapsrdet project: "Disclosure risk and transparency in big data privacy" (VR 2016-03346, 2017-2020). DRIAT
This book constitutes the refereed proceedings of the First International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2004, held in Barcelona, Spain in August 2004. The 26 revised full papers presented together with 4 invited papers were carefully reviewed and selected from 53 submissions. The papers are devoted to topics like models for information fusion, aggregation operators, model selection, fuzzy integrals, fuzzy sets, fuzzy multisets, neural learning, rule-based classification systems, fuzzy association rules, algorithmic learning, diagnosis, text categorization, unsupervised aggregation, the Choquet integral, group decision making, preference relations, vague knowledge processing, etc