Privacy Preserving Clustering by Data Transformation

By Stanley R. M. Oliveira, Osmar R Zaiane. Journal of Information and Data Management, 2010

373 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/59282c3df6d5718a9c52047ed035a235c9d248ed

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Charu C. Aggarwal
2. Philip S. Yu
3. Elisa Bertino
4. Isabel Wagner
5. Wei Jiang
6. Kun Liu
7. Dan Lin
8. David Eckhoff

Citations in the corpus

• Privacy-Preserving SVM Classification on Vertically Partitioned Data (2006, unknown): “Recently, some alternative techniques such as condensation[13] and transformation [14] have also been proposed.”
• Privacy-preserving SVM classification (2008, unknown): “Recently, some alternative techniques such as condensation [2] and transformation [27] have also been proposed, though the security properties of these need to be carefully investigated.”
• Distributed Clustering in the Anonymized Space with Local Differential Privacy (2019, background): “The additive data perturbation (ADP [24]) and the random subspace projection (RSP [25]) are two of the most common approaches to transfer original data to the anonymized space in the literature.”
• A General Survey of Privacy-Preserving Data Mining Models and Algorithms (2008, methodology): “In particular, the approach is discussed in detail in [97, 98], in which it is shown how to use the method for privacy-preserving clustering.”
• Privacy-preserving distributed k-means clustering over arbitrarily partitioned data (2005, methodology): “Oliviera and Zaiane [14] addressed the privacy-preserving data clustering problem using randomization techniques.”
• A Survey of Quantification of Privacy Preserving Data Mining Algorithms (2008, background): “For example, in [21], Oliveira and Zaiane define two metrics misses cost and artifactual pattern which are corresponding to lost information and artifactual information respectively.”
• Secure kNN computation on encrypted databases (2009, methodology): “For security, all the tuples in DB are encrypted by player 1 before they are exported to the EDBMS.”
• Technical Privacy Metrics (2015, methodology): “Databases [144] 5.3.12 Historicalk-anonymity Location [20] 5.3.13 Cluster similarity Smart metering [75] 5.3.14 Coefficient of determinationR2 Smart metering [75] 5.3.15 Normalized variance Databases [110] ACM Computing Surveys, Vol. 51, No. 3, Article 57. Publication date: June 2018. Technical Priv”
• Random projection-based multiplicative data perturbation for privacy preserving distributed data mining (2006, background): “In this regard, the security of a similar approach using random rotation [29] to protect the data privacy is also questionable.”