Deriving private information from randomized data

By Zhengli Huang, Wenliang Du, Biao Chen. ACM SIGMOD Conference, 2005

510 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ce039f4701c5b34ee945443f253ef0031aafe300

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Citations in the corpus

Privacy preserving data classification with rotation perturbation (2005, methodology): “…inference); (2) approximately reconstructing the data from the perturbed data and then estimating the original data from the reconstructed data [8] [6] (approximation-based inference); and (3) if the distributions of the original columns are known, the values or the properties of the values in…”
L-diversity: privacy beyond k-anonymity (2006, unknown): “[ Huang et al. 2004 ] show that the correlation between attributes is the key factor behind the attacks proposed in [Kargupta et al. 2003].”
Privacy preservation in wireless sensor networks: A state-of-the-art survey (2009, background): “Note that an alert communication originating from a patient’s heart monitor in the medical WSN is enough for an adversary to infer that the patient suffers from heart problem.”
Privacy-Preserving SVM Classification on Vertically Partitioned Data (2006, background): “However, there is some debate about the security properties of such algorithms [9, 10].”
A General Survey of Privacy-Preserving Data Mining Models and Algorithms (2008, unknown): “One advantage of this technique is that the lower order marginal totals of the data are completely preserved and are not perturbed at all.”
The applicability of the perturbation based privacy preserving data mining for real-world data (2008, background): “Since then many good approaches have been proposed [3] [5] [6].”
PDA: Privacy-Preserving Data Aggregation in Wireless Sensor Networks (2007, background): “in [21], certain types of data perturbation might not preserve privacy well.”
A Random Rotation Perturbation Approach to Privacy Preserving Data Classification (2005, background): “[14, 11] observed that the spectral properties of the randomized data can be utilized to separate noise from the private data.”
A survey on privacy in mobile participatory sensing applications (2011, result): “As demonstrated in [76], an analysis of the reported data in conjunction with the reporting patterns may allow identifying the participants’ residences among other significant places, such as workplaces and favorite entertainment centers, based on their location traces.”
PrivateClean: Data Cleaning and Differential Privacy (2016, result): “This work led to several other results such as algorithms for classiﬁcation on private data [10], statistical reconstruction of a dataset [18].”
Future Data and Security Engineering (2014, methodology): “In [3], a new framework for query reformulation was introduced that uses multiple information sources (a large web corpus, articles and titles from Wikipedia) for weighting the explicit query concepts as well as selecting relevant and diverse set of weighted expansion terms.”
L-diversity: Privacy beyond k-anonymity (2007, unknown): “[Huang et al. 2004] show that the correlation between attributes is the key factor behind the attacks proposed in [Kargupta et al.”
Privacy-preserving SVM classification (2008, unknown): “However, several problems have been pointed out with the privacy inherent in such an approach [15, 19, 20, 24].”
Cloud-enabled privacy-preserving collaborative learning for mobile sensing (2012, methodology): “However, existing additive noise filtering techniques (such as spectral analysis [26], principal component analysis, and Bayes estimation [22]) need to know at least the approximate mean and theapproximate covariance of the additive noise.”
Privacy-preserving decision trees over vertically partitioned data (2005, background): “However, recently there has been debate about the security properties of such algorithms [Kargupta et al. 2003; Huang et al. 2005].”