On Private Scalar Product Computation for Privacy-Preserving Data Mining

By Bart Goethals, Sven Laur, Helger Lipmaa, Taneli Mielikäinen. 2004

1,924 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Privacy & Security

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Citations in the corpus

Federated Machine Learning (2019, methodology): “The methods of differential privacy, k-anonymity, and diversification [3] involve adding noise to the data, or using generalizationmethods to obscure certain sensitive attributes until the third party cannot distinguish the individual, therebymaking the data impossible to be restore to protect user ”
Personalized and Private Peer-to-Peer Machine Learning (2017, background): “These pairwise similarity weights may be derived from user profiles (e.g., in the health domain: weight, size, diabetes type, etc.) or directly from the local datasets, and can be computed in a private way (see e.g., Goethals et al., 2004; Alaggan et al., 2011).”
Federated Learning: Challenges, Methods, and Future Directions (2019, background): “Privacy-preserving learning has been extensively studied by the machine learning [e.g., 76], systems [e.g., 4, 11], and theory [e.g., 38, 69] communities.”
I-Pic: A Platform for Privacy-Compliant Image Capture (2016, methodology): “The Capture Agent and the Bystander Agent compare extracted features and a bystander’s classifier weight vector by implementing a secure dot-product protocol [8] followed by a secure threshold comparison protocol based on garbled circuits [9].”
Research Priorities for Robust and Beneficial Artificial Intelligence (2015, background): “Privacy How should the ability of AI systems to interpret the data obtained from surveillance cameras, phone lines, emails, and so on, interact with the right to privacy? How will privacy risks interact with cybersecurity and cyberwarfare (Singer and Friedman 2014)? Our ability to take full advantag”
Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification (2004, methodology): “In general, there are two different approaches: one is to use data distortion approach, in which data are disguised by the added noises [1, 2, 13, 14, 22, 23].”
A privacy-preserving algorithm for distributed training of neural network ensembles (2012, methodology): “To solve the problem we have, the secure scalar product algorithm [20] is used for several times in our paper.”
Privacy-preserving decision trees over vertically partitioned data (2005, background): “There has been growing interest in privacy-preserving data mining since the seminal papers in 2000 [ 1 , 2]. Classication is one of the most ubiquitous data mining problems found in real life.”
A Random Rotation Perturbation Approach to Privacy Preserving Data Classification (2005, methodology): “Hence, the quality of preserving column distribution becomes the key in applying randomization approach [ 3 ] to decision tree model.”
Data privacy through optimal k-anonymization (2005, background): “An attribute generalization for an attribute with ordered value domain is a partitioning of the attribute domain into intervals such that every value of appears in some interval, and every value in an interval precedes every value in the intervals following . For example, a generalization of an “age”
Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning (2016, methodology): “The former was proposed by Agrawal and Srikant which preserves the private data by adding noise to the source data [50], while the latter was proposed by Lindell and Pinkas using cryptograhpic methods to prevent the disclosure of the private data [51].”
A Practical Differentially Private Random Decision Tree Classifier (2009, background): “In contrast to the privacy-preserving decision tree construction methods presented in [10], [2], these decision trees provide provable privacy guarantees about any individual’s contribution to the final result.”
Practical privacy: the SuLQ framework (2005, background): “In 2000 Lindell and Pinkas [11] and Agrawal and Srikant [2] presented two approaches to privacy preserving datamining.”
InPrivate Digging: Enabling Tree-based Distributed Data Mining with Differential Privacy (2018, background): “Privacy-preserving data mining has attracted much attention over the last decades, and many techniques have been developed to enable privacy preservation of data, such as kanonymity [16], perturbation-based schemes [17] and cryptographic tools.”
Privacy-Preserving Datamining on Vertically Partitioned Databases (2004, background): “Agrawal and Srikant began to address privacy in datamining in 2000 [3].”