A General Survey of Privacy-Preserving Data Mining Models and Algorithms

By Charu C. Aggarwal, Philip S. Yu. 2008

556 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science

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Citations in the corpus

Privacy Preserving Data Mining (2015, background): “These methods reduce risk of identiﬁcation with public records, while reducing applications accuracy on transformed data (Aggarwal and Philip, 2008).”
Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption (2017, background): “More practical tasks include privacy-preserving data mining [3, 4], ubiquitous computing [36], and social network analysis [64].”
PrivLogit: Efficient Privacy-preserving Logistic Regression by Tailoring Numerical Optimizers (2016, methodology): “…algorithms (e.g., Newton method) and focusing on accelerating the underlying cryptography alone (pervasive among nearly all related secure protocols [Aggarwal and Philip, 2008]), we propose to tailor numerical optimization specially for cryptographic computing in general (but not tied to any…”
Synthetic data generation for tabular health records: A systematic review (2022, background): “…data publishing (PPDP) that could allow for data pooling, in recent years, research has targeted distributed privacy-preserving data mining (aggregating distributed analytics results) and machine learning model (federated learning) training as a means to avoid data sharing [2, 3].”
Deep Private-Feature Extraction (2018, background): “…this trend and extended this idea, however, this approach faces two important obstacles: curse of dimensionality and non-robustness to attacks [11], which make it inefﬁcient for high dimensional data with side informations. k -anonymity is another popular option for addressing the problem of…”
A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics (2017, background): “addition [18], [29] and k-anonymity [15] extended by generalization and suppression [31]–[33] are proposed and surveyed in [34].”
Provably Fair Representations (2017, background): “(Yˆ = 1|S = 1) . We assume p(Yˆ = 1|S = 1)≥p(Yˆ = 1|S = 0)without loss of generality, since the assignment of sensitive variable groups to the values 0 and 1 is arbitrary. Therefore SP(Yˆ,S),DI(Yˆ,S)∈[0,1] where 0 is perfectly fair and 1 is maximally unfair. 3.1.1 Statistical parity guarantee. The s”
On the features and challenges of security and privacy in distributed internet of things (2013, background): “As data will be distributed amongst various entities, it also is necessary to study the applicability of existing privacy-preserving distributed data mining algorithms [43].”
A comprehensive review on privacy preserving data mining (2015, methodology): “The main rationale for implementing such techniques is to maintain individuals’ privacy while deriving collective results over the entire data (Aggarwal and Yu 2008).”
PrivateClean: Data Cleaning and Differential Privacy (2016, background): “There are a number of surveys describing deterministic techniques for privacy preserving analytics [4,13].”
Internet of Things: An Overview (2017, background): “Considering the latter approach, since each entity can access only chunks of data, distributed privacy preserving algorithms have been developed to handle data scattering and their corresponding privacy tags[82].”
PrivEdge: From Local to Distributed Private Training and Prediction (2020, background): “honest in performing the operations, but also attempting to learn information about other users by analysing the received information [16]) or by an honest-but-curious service provider during training or prediction [13], [17]–[19].”