State-of-the-art in privacy preserving data mining

By Vassilios S. Verykios, Elisa Bertino, Igor Nai Fovino, Loredana Parasiliti Provenza, Yücel Saygin, Yannis Theodoridis. 2004

996 citations recorded by the source corpus.

Subfields: Artificial Intelligence

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In Artificial Intelligence

Citations in the corpus

Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers (2013, background): “In [55], Verykios et al. classiﬁed PPDM techniques in ﬁve classes.”
Privacy-Preserving SVM Classification on Vertically Partitioned Data (2006, background): “A good overview of prior work in this area can be found in [12].”
Big Data: A Survey (2014, background): “Driven by privacy protection in e-commerce, e-government, and health care applications, privacy protection data mining is an emerging research field [120].”
Privacy preserving crowd monitoring: Counting people without people models or tracking (2008, background): “This tension is common in all areas of data-mining [1, 2], but becomes an especially acute problem for computer vision for two reasons: 1) the perception of compromised privacy is particularly strong for technology which, by default, keeps a visual record of people’s actions; 2) the current approach”
Differential Privacy for Clinical Trial Data: Preliminary Evaluations (2009, background): “, [8]) and statistica l disclosure limitation (e.”
Security and privacy in the Internet of Things: Current status and open issues (2014, background): “• Lightweight key management systems to enable the establishment of trust relationships and distribution of encryption materials using minimum communication and processing resources, consistent with the resourceconstrained nature of many IoT devices.”
Privacy-preserving SVM classification (2008, neutral): “A good overview of prior work in this area can be found in [40, 42].”
Privacy-Preserving Data Linkage and Geocoding: Current Approaches and Research Directions (2006, methodology): “Techniques being developed in privacypreserving data mining [14] will have to be modi ed in order to become suitable for data linkage applications.”
Open Civic Data: Of the People, For the People, By the People (2014, background): “Research on crowdsourcing [44] and data privacy & anonymization [29, 45] is highly relevant.”
Random projection-based multiplicative data perturbation for privacy preserving distributed data mining (2006, background): “An overview of the state-of-the-art privacy preserving data mining techniques is presented in [20].”
A rigorous and customizable framework for privacy (2012, background): “, [9, 7, 31, 38, 37, 28, 8, 2, 39, 10, 36, 29]) that are deemed to be sensitive.”
A Survey of Quantification of Privacy Preserving Data Mining Algorithms (2008, methodology): “Moreover, as pointed out in [32], it is important not to forget that intruders and data terrorists will try to compromise information by using various data mining algorithms.”
PrivLogit: Efficient Privacy-preserving Logistic Regression by Tailoring Numerical Optimizers (2016, methodology): “…databases (belonging to different organizations) is widely used in various disciplines, such as in data management [Zhang and Zhao, 2005, Verykios et al., 2004, Aggarwal and Philip, 2008], biomedical and social sciences [McCarty et al., 2011, Zheng et al., 2017] where data are…”
A privacy threat analysis framework: supporting the elicitation and fulfillment of privacy requirements (2011, background): “Privacy preserving data mining [63, 64] 9 9 9”
A Framework for Evaluating Privacy Preserving Data Mining Algorithms* (2005, methodology): “In this section, we (cid:30)rst present a taxonomy of the PPDM algorithms that have been proposed based on a classi(cid:30)cation presented in Verykios et al. (2004).”
Distributed learning for Random Vector Functional-Link networks (2015, background): “Finally, it may happen that data simply cannot be moved across the network: either for being large (in term of number of examples or dimensionality of each pattern), or because fundamental privacy concerns are present [40].”