WaveCluster is an important family of grid-based clustering algorithms that are capable of finding clusters of arbitrary shapes. In this paper, we investigate techniques to perform WaveCluster while ensuring differential privacy. Our goal is to develop a general technique for achieving differential privacy on WaveCluster that accommodates different wavelet transforms. We show that straightforward techniques based on synthetic data generation and introduction of random noise when quantizing the data, though generally preserving the distribution of data, often introduce too much noise to preserve useful clusters. We then propose two optimized techniques, PrivTHR and PrivTHREM, which can significantly reduce data distortion during two key steps of WaveCluster: the quantization step and the significant grid identification step. We conduct extensive experiments based on four datasets that are particularly interesting in the context of clustering, and show that PrivTHR and PrivTHREM achieve high utility when privacy budgets are properly allocated.
In this work, our goal is to perform WaveCluster under differential privacy. The focus of initial work on differential privacy @cite_35 @cite_13 @cite_5 @cite_9 @cite_20 concerned the theoretical proof of its feasibility on various data analysis tasks, e.g., histogram and logistic regression.
@cite_35 @cite_9 @cite_5 @cite_13 @cite_20
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Differential privacy is a strong notion for protecting individual privacy in privacy preserving data analysis or publishing. In this paper, we study the problem of differentially private histogram release based on an interactive differential privacy interface. We propose two multidimensional partitioning strategies including a baseline cell-based partitioning and an innovative kd-tree based partitioning. In addition to providing formal proofs for differential privacy and usefulness guarantees for linear distributive queries, we also present a set of experimental results and demonstrate the feasibility and performance of our method. Differential privacy has emerged as one of the most promising privacy models for private data release. It can be used to release different types of data, and, in particular, histograms, which provide useful summaries of a dataset. Several differentially private histogram releasing schemes have been proposed recently. However, most of them directly add noise to the histogram counts, resulting in undesirable accuracy. In this paper, we propose two sanitization techniques that exploit the inherent redundancy of real-life datasets in order to boost the accuracy of histograms. They lossily compress the data and sanitize the compressed data. Our first scheme is an optimization of the Fourier Perturbation Algorithm (FPA) presented in RN10 . It improves the accuracy of the initial FPA by a factor of 10. The other scheme relies on clustering and exploits the redundancy between bins. Our extensive experimental evaluation over various real-life and synthetic datasets demonstrates that our techniques preserve very accurate distributions and considerably improve the accuracy of range queries over attributed histograms. This work studies formal utility and privacy guarantees for a simple multiplicative database transformation, where the data are compressed by a random linear or affine transformation, reducing the number of data records substantially, while preserving the number of original input variables. We provide an analysis framework inspired by a recent concept known as differential privacy (Dwork 06). Our goal is to show that, despite the general difficulty of achieving the differential privacy guarantee, it is possible to publish synthetic data that are useful for a number of common statistical learning applications. This includes high dimensional sparse regression ( 07), principal component analysis (PCA), and other statistical measures ( 06) based on the covariance of the initial data. Privacy-preserving data publishing has attracted considerable research interest in recent years. Among the existing solutions, ∈-differential privacy provides the strongest privacy guarantee. Existing data publishing methods that achieve ∈-differential privacy, however, offer little data utility. In particular, if the output data set is used to answer count queries, the noise in the query answers can be proportional to the number of tuples in the data, which renders the results useless. In this paper, we develop a data publishing technique that ensures ∈-differential privacy while providing accurate answers for range-count queries, i.e., count queries where the predicate on each attribute is a range. The core of our solution is a framework that applies wavelet transforms on the data before adding noise to it. We present instantiations of the proposed framework for both ordinal and nominal data, and we provide a theoretical analysis on their privacy and utility guarantees. In an extensive experimental study on both real and synthetic data, we show the effectiveness and efficiency of our solution.