Recent advances in computing have allowed for the possibility to collect large amounts of data on personal activities and private living spaces. Collecting and publishing a dataset in this environment can cause concerns over privacy of the individuals in the dataset. In this paper we examine these privacy concerns. In particular, given a target application, how can we mask sensitive attributes in the data while preserving the utility of the data in that target application. Our focus is on protecting attributes that are hidden and can be inferred from the data by machine learning algorithms. We propose a generic framework that (1) removes the knowledge useful for inferring sensitive information, but (2) preserves the knowledge relevant to a given target application. We use deep neural networks and generative adversarial networks (GAN) to create privacy-preserving perturbations. Our noise-generating network is compact and efficient for running on mobile devices. Through extensive experiments, we show that our method outperforms conventional methods in effectively hiding the sensitive attributes while guaranteeing high performance for the target application. Our results hold for new neural network architectures, not seen before during training and are suitable for training new classifiers.
A different approach to preserve privacy is making sure sensitive elements of the data is removed before publishing it, often called privacy-preserving data publishing (PPDP) @cite_15 . A main line of work in this field focuses on transforming numerical data into a secondary feature space, such that certain statistical properties are preserved and data mining tasks can be done with minimal performance loss @cite_43 @cite_8 @cite_9 . These methods guarantee data utility only on this secondary space. This means that a classifier trained on the perturbed data is not guaranteed to perform well on original data. This can be troublesome in a scenario where a classification model is trained on public, perturbed data and is going to be deployed on users' personal devices to perform a task locally on non-perturbed private user data. Our perturbed data can be used in conjunction with original data in specified target applications. In addition, some of the methods in this category rely on expensive computations which renders them infeasible on large datasets.
@cite_43 @cite_15 @cite_9 @cite_8
129088861 2129870696 2129533844 2005107218
This paper presents a random rotation perturbation approach for privacy preserving data classification. Concretely, we identify the importance of classificationspecific information with respect to the loss of information factor, and present a random rotation perturbation framework for privacy preserving data classification. Our approach has two unique characteristics. First, we identify that many classification models utilize the geometric properties of datasets, which can be preserved by geometric rotation. We prove that the three types of classifiers will deliver the same performance over the rotation perturbed dataset as over the original dataset. Second, we propose a multi-column privacy model to address the problems of evaluating privacy quality for multidimensional perturbation. With this metric, we develop a local optimal algorithm to find the good rotation perturbation in terms of privacy guarantee. We also analyze both naive estimation and ICA-based reconstruction attacks with the privacy model. Our initial experiments show that the random rotation approach can provide high privacy guarantee while maintaining zero-loss of accuracy for the dis- 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. 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 Privacy-preserving data publishing addresses the problem of disclosing sensitive data when mining for useful information. Among the existing privacy models, ∈-differential privacy provides one of the strongest privacy guarantees and has no assumptions about an adversary's background knowledge. Most of the existing solutions that ensure ∈-differential privacy are based on an interactive model, where the data miner is only allowed to pose aggregate queries to the database. In this paper, we propose the first anonymization algorithm for the non-interactive setting based on the generalization technique. The proposed solution first probabilistically generalizes the raw data and then adds noise to guarantee ∈-differential privacy. As a sample application, we show that the anonymized data can be used effectively to build a decision tree induction classifier. Experimental results demonstrate that the proposed non-interactive anonymization algorithm is scalable and performs better than the existing solutions for classification analysis.