Deep learning (DL) is a promising area of machine learning which is becoming popular due to its remarkable accuracy when trained with a massive amount of data. Often, these datasets are highly sensitive crowd-sourced data such as medical data, financial data, or image data, and the DL models trained on these data tend to leak privacy. We propose a new local differentially private (LDP) algorithm (named LATENT) which redesigns the training process in a way that a data owner can add a randomization layer before data leave data owners' devices and reach to a potentially untrusted machine learning service. This way LATENT prevents privacy leaks of DL models, e.g., due to membership inference and memorizing model attacks, while providing excellent accuracy. By not requiring a trusted party, LATENT can be more practical for cloud-based machine learning services in comparison to existing differentially private approaches. Our experimental evaluation of LATENT on convolutional deep neural networks demonstrates excellent accuracy (e.g. 91 - 96 ) with high model quality even under very low privacy budgets (e.g. @math ), outperforming existing differentially private approaches for deep learning.
A main challenge in privacy-preserving data mining (PPDM) is countering the capabilities of skilled adversaries @cite_37 @cite_40 @cite_38 @cite_2 . Data modification (data perturbation) @cite_29 @cite_57 and encryption @cite_50 @cite_36 are two main approaches to PPDM. Methods based on encryption provide good security and accuracy. However, cryptographic methods often suffer from high computational complexity which make them unsuitable for large-scale data mining @cite_33 . Compared to encryption, perturbation utilizes lower computational complexity, which makes it effective for big data mining @cite_23 . Examples for perturbation techniques include noise addition @cite_30 , geometric transformation @cite_28 , randomization @cite_56 , condensation @cite_11 , hybrid perturbation (uses several perturbation techniques together) @cite_43 .
@cite_30 @cite_38 @cite_37 @cite_33 @cite_36 @cite_28 @cite_29 @cite_56 @cite_57 @cite_43 @cite_40 @cite_50 @cite_2 @cite_23 @cite_11
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We focus primarily on the use of additive and matrix multiplicative data perturbation techniques in privacy preserving data mining (PPDM). We survey a recent body of research aimed at better understanding the vulnerabilities of these techniques. These researchers assumed the role of an attacker and developed methods for estimating the original data from the perturbed data and any available prior knowledge. Finally, we briefly discuss research aimed at attacking k-anonymization, another data perturbation technique in PPDM. Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms, has been developed by the research community working on security and knowledge discovery. The aim of these algorithms is the extraction of relevant knowledge from large amount of data, while protecting at the same time sensitive information. Several data mining techniques, incorporating privacy protection mechanisms, have been developed that allow one to hide sensitive itemsets or patterns, before the data mining process is executed. Privacy preserving classification methods, instead, prevent a miner from building a classifier which is able to predict sensitive data. Additionally, privacy preserving clustering techniques have been recently proposed, which distort sensitive numerical attributes, while preserving general features for clustering analysis. A crucial issue is to determine which ones among these privacy-preserving techniques better protect sensitive information. However, this is not the only criteria with respect to which these algorithms can be evaluated. It is also important to assess the quality of the data resulting from the modifications applied by each algorithm, as well as the performance of the algorithms. There is thus the need of identifying a comprehensive set of criteria with respect to which to assess the existing PPDM algorithms and determine which algorithm meets specific requirements. In this paper, we present a first evaluation framework for estimating and comparing different kinds of PPDM algorithms. Then, we apply our criteria to a specific set of algorithms and discuss the evaluation results we obtain. Finally, some considerations about future work and promising directions in the context of privacy preservation in data mining are discussed. Privacy preserving data mining has become increasingly popular because it allows sharing of privacy-sensitive data for analysis purposes. However, existing techniques such as random perturbation do not fare well for simple yet widely used and efficient Euclidean distance-based mining algorithms. Although original data distributions can be pretty accurately reconstructed from the perturbed data, distances between individual data points are not preserved, leading to poor accuracy for the distance-based mining methods. Besides, they do not generally focus on data reduction. Other studies on secure multi-party computation often concentrate on techniques useful to very specific mining algorithms and scenarios such that they require modification of the mining algorithms and are often difficult to generalize to other mining algorithms or scenarios. This paper proposes a novel generalized approach using the well-known energy compaction power of Fourier-related transforms to hide sensitive data values and to approximately preserve Euclidean distances in centralized and distributed scenarios to a great degree of accuracy. Three algorithms to select the most important transform coefficients are presented, one for a centralized database case, the second one for a horizontally partitioned, and the third one for a vertically partitioned database case. Experimental results demonstrate the effectiveness of the proposed approach. Data perturbation is a popular technique in privacy-preserving data mining. A major challenge in data perturbation is to balance privacy protection and data utility, which are normally considered as a pair of conflicting factors. We argue that selectively preserving the task model specific information in perturbation will help achieve better privacy guarantee and better data utility. One type of such information is the multidimensional geometric information, which is implicitly utilized by many data-mining models. To preserve this information in data perturbation, we propose the Geometric Data Perturbation (GDP) method. In this paper, we describe several aspects of the GDP method. First, we show that several types of well-known data-mining models will deliver a comparable level of model quality over the geometrically perturbed data set as over the original data set. Second, we discuss the intuition behind the GDP method and compare it with other multidimensional perturbation methods such as random projection perturbation. Third, we propose a multi-column privacy evaluation framework for evaluating the effectiveness of geometric data perturbation with respect to different level of attacks. Finally, we use this evaluation framework to study a few attacks to geometrically perturbed data sets. Our experimental study also shows that geometric data perturbation can not only provide satisfactory privacy guarantee but also preserve modeling accuracy well. Protection of privacy has become an important problem in data mining. In particular, individuals have become increasingly unwilling to share their data, frequently resulting in individuals either refusing to share their data or providing incorrect data. In turn, such problems in data collection can affect the success of data mining, which relies on sufficient amounts of accurate data in order to produce meaningful results. Random perturbation and randomized response techniques can provide some level of privacy in data collection, but they have an associated cost in accuracy. Cryptographic privacy-preserving data mining methods provide good privacy and accuracy properties. However, in order to be efficient, those solutions must be tailored to specific mining tasks, thereby losing generality.In this paper, we propose efficient cryptographic techniques for online data collection in which data from a large number of respondents is collected anonymously, without the help of a trusted third party. That is, our solution allows the miner to collect the original data from each respondent, but in such a way that the miner cannot link a respondent's data to the respondent. An advantage of such a solution is that, because it does not change the actual data, its success does not depend on the underlying data mining problem. We provide proofs of the correctness and privacy of our solution, as well as experimental data that demonstrates its efficiency. We also extend our solution to tolerate certain kinds of malicious behavior of the participants. In recent years, privacy preserving data mining has become an important problem because of the large amount of personal data which is tracked by many business applications. An important method for privacy preserving data mining is the method of condensation. This method is often used in the case of multi-dimensional data in which pseudo-data is generated to mask the true values of the records. However, these methods are not easily applicable to the case of string data, since they require the use of multi-dimensional statistics in order to generate the pseudo-data. String data are especially important in the privacy preserving data-mining domain because most DNA and biological data are coded as strings. In this article, we will discuss a new method for privacy preserving mining of string data with the use of simple template-based models. The template-based model turns out to be effective in practice, and preserves important statistical characteristics of the strings such as intra-record distances. We will explore the behavior in the context of a classification application, and show that the accuracy of the application is not affected significantly by the anonymization process. Data perturbation is a popular technique for privacypreserving data mining. The major challenge of data perturbation is balancing privacy protection and data quality, which are normally considered as a pair of contradictive factors. We propose that selectively preserving only the task model specific information in perturbation would improve the balance. Geometric data perturbation, consisting of random rotation perturbation, random translation perturbation, and noise addition, aims at preserving the important geometric properties of a multidimensional dataset, while providing better privacy guarantee for data classification modeling. The preliminary study has shown that random geometric perturbation can well preserve model accuracy for several popular classification models, including kernel methods, linear classifiers, and SVM classifiers, while it also revealed some security concerns to random geometric perturbation. In this paper, we address some potential attacks to random geometric perturbation and design several methods to reduce the threat of these attacks. Experimental study shows that the enhanced geometric perturbation can provide satisfactory privacy guarantee while still well preserving model accuracy for the discussed data classification models. Despite its benefit in a wide range of applications, data mining techniques also have raised a number of ethical issues. Some such issues include those of privacy, data security, intellectual property rights, and many others. In this paper, we address the privacy problem against unauthorized secondary use of information. To do so, we introduce a family of geometric data transformation methods (GDTMs) which ensure that the mining process will not violate privacy up to a certain degree of security. We focus primarily on privacy preserving data clustering, notably on partition-based and hierarchical methods. Our proposed methods distort only confidential numerical attributes to meet privacy requirements, while preserving general features for clustering analysis. Our experiments demonstrate that our methods are effective and provide acceptable values in practice for balancing privacy and accuracy. We report the main results of our performance evaluation and discuss some open research issues. Due to growing concerns about the privacy of personal information, organizations that use their customers' records in data mining activities are forced to take actions to protect the privacy of the individuals. A frequently used disclosure protection method is data perturbation. When used for data mining, it is desirable that perturbation preserves statistical relationships between attributes, while providing adequate protection for individual confidential data. To achieve this goal, we propose a kd-tree based perturbation method, which recursively partitions a data set into smaller subsets such that data records within each subset are more homogeneous after each partition. The confidential data in each final subset are then perturbed using the subset average. An experimental study is conducted to show the effectiveness of the proposed method In recent years, privacy preserving data mining has become an important problem because of the large amount of personal data which is tracked by many business applications. In many cases, users are unwilling to provide personal information unless the privacy of sensitive information is guaranteed. In this paper, we propose a new framework for privacy preserving data mining of multi-dimensional data. Previous work for privacy preserving data mining uses a perturbation approach which reconstructs data distributions in order to perform the mining. Such an approach treats each dimension independently and therefore ignores the correlations between the different dimensions. In addition, it requires the development of a new distribution based algorithm for each data mining problem, since it does not use the multi-dimensional records, but uses aggregate distributions of the data as input. This leads to a fundamental re-design of data mining algorithms. In this paper, we will develop a new and flexible approach for privacy preserving data mining which does not require new problem-specific algorithms, since it maps the original data set into a new anonymized data set. This anonymized data closely matches the characteristics of the original data including the correlations among the different dimensions. We present empirical results illustrating the effectiveness of the method. This paper explores the possibility of using multiplicative random projection matrices for privacy preserving distributed data mining. It specifically considers the problem of computing statistical aggregates like the inner product matrix, correlation coefficient matrix, and Euclidean distance matrix from distributed privacy sensitive data possibly owned by multiple parties. This class of problems is directly related to many other data-mining problems such as clustering, principal component analysis, and classification. This paper makes primary contributions on two different grounds. First, it explores independent component analysis as a possible tool for breaching privacy in deterministic multiplicative perturbation-based models such as random orthogonal transformation and random rotation. Then, it proposes an approximate random projection-based technique to improve the level of privacy protection while still preserving certain statistical characteristics of the data. The paper presents extensive theoretical analysis and experimental results. Experiments demonstrate that the proposed technique is effective and can be successfully used for different types of privacy-preserving data mining applications. Privacy-preserving decision tree mining is an important problem that has yet to be thoroughly understood. In fact, the privacy-preserving decision tree mining method explored in the pioneer paper [1] was recently showed to be completely broken, because its data perturbation technique is fundamentally flawed [2]. However, since the general framework presented in [1] has some nice and useful features in practice, it is natural to ask if it is possible to rescue the framework by, say, utilizing a different data perturbation technique. In this paper, we answer this question affirmatively by presenting such a data perturbation technique based on random substitutions. We show that the resulting privacy-preserving decision tree mining method is immune to attacks (including the one introduced in [2]) that are seemingly relevant. Systematic experiments show that it is also effective. We examine the tradeoff between privacy and usability of statistical databases. We model a statistical database by an n-bit string d 1 ,..,d n , with a query being a subset q ⊆ [n] to be answered by Σ ieq d i . Our main result is a polynomial reconstruction algorithm of data from noisy (perturbed) subset sums. Applying this reconstruction algorithm to statistical databases we show that in order to achieve privacy one has to add perturbation of magnitude (Ω√n). That is, smaller perturbation always results in a strong violation of privacy. We show that this result is tight by exemplifying access algorithms for statistical databases that preserve privacy while adding perturbation of magnitude O(√n).For time-T bounded adversaries we demonstrate a privacypreserving access algorithm whose perturbation magnitude is ≈ √T. Frequent sequential pattern mining is a central task in many fields such as biology and finance. However, release of these patterns is raising increasing concerns on individual privacy. In this paper, we study the sequential pattern mining problem under the differential privacy framework which provides formal and provable guarantees of privacy. Due to the nature of the differential privacy mechanism which perturbs the frequency results with noise, and the high dimensionality of the pattern space, this mining problem is particularly challenging. In this work, we propose a novel two-phase algorithm for mining both prefixes and substring patterns. In the first phase, our approach takes advantage of the statistical properties of the data to construct a model-based prefix tree which is used to mine prefixes and a candidate set of substring patterns. The frequency of the substring patterns is further refined in the successive phase where we employ a novel transformation of the original data to reduce the perturbation noise. Extensive experiment results using real datasets showed that our approach is effective for mining both substring and prefix patterns in comparison to the state-of-the-art solutions. In recent years, the wide availability of personal data has made the problem of privacy preserving data mining an important one. A number of methods have recently been proposed for privacy preserving data mining of multidimensional data records. One of the methods for privacy preserving data mining is that of anonymization, in which a record is released only if it is indistinguishable from k other entities in the data. We note that methods such as k-anonymity are highly dependent upon spatial locality in order to effectively implement the technique in a statistically robust way. In high dimensional space the data becomes sparse, and the concept of spatial locality is no longer easy to define from an application point of view. In this paper, we view the k-anonymization problem from the perspective of inference attacks over all possible combinations of attributes. We show that when the data contains a large number of attributes which may be considered quasi-identifiers, it becomes difficult to anonymize the data without an unacceptably high amount of information loss. This is because an exponential number of combinations of dimensions can be used to make precise inference attacks, even when individual attributes are partially specified within a range. We provide an analysis of the effect of dimensionality on k-anonymity methods. We conclude that when a data set contains a large number of attributes which are open to inference attacks, we are faced with a choice of either completely suppressing most of the data or losing the desired level of anonymity. Thus, this paper shows that the curse of high dimensionality also applies to the problem of privacy preserving data mining.