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.
@cite_30: The security of organizational databases has received considerable attention in the literature in recent years. This can be attributed to a simultaneous increase in the amount of data being stored in databases, the analysis of such data, and the desire to protect confidential data. Data perturbation methods are often used to protect confidential, numerical data from unauthorized queries while providing maximum access and accurate information to legitimate queries. To provide accurate information, it is desirable that perturbation does not result in a change in relationships between attributes. In the presence of nonconfidential attributes, existing methods will result in such a change. This study describes a new method (General Additive Data Perturbation) that does not change relationships between attributes. All existing methods of additive data perturbation are shown to be special cases of this method. When the database has a multivariate normal distribution, the new method provides maximum security and minimum bias. For nonnormal databases, the new method provides better security and bias performance than the multiplicative data perturbation method.
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@cite_37: We study the distributed privacy preserving data collection problem: an untrusted data collector (e.g., a medical research institute) wishes to collect data (e.g., medical records) from a group of respondents (e.g., patients). Each respondent owns a multi-attributed record which contains both non-sensitive (e.g., quasi-identifiers) and sensitive information (e.g., a particular disease), and submits it to the data collector. Assuming T is the table formed by all the respondent data records, we say that the data collection process is privacy preserving if it allows the data collector to obtain a k-anonymized or l-diversified version of T without revealing the original records to the adversary. We propose a distributed data collection protocol that outputs an anonymized table by generalization of quasi-identifier attributes. The protocol employs cryptographic techniques such as homomorphic encryption, private information retrieval and secure multiparty computation to ensure the privacy goal in the process of data collection. Meanwhile, the protocol is designed to leak limited but noncritical information to achieve practicability and efficiency. Experiments show that the utility of the anonymized table derived by our protocol is in par with the utility achieved by traditional anonymization techniques.
@cite_33: Privacy issues have become a considerable issue while the applications of big data are growing dramatically fast in cloud computing. The benefits us implementing these emerging technologies have improved or changed service models and improve application performances in various perspectives. However, the remarkably growing volume of data sizes has also resulted in many challenges in practice. The time execution of encrypting data is one of the serious issues during the processes of data processing and transmissions. Many current applications abandon data encryptions in order to reach an adoptive performance level, companions with privacy concerns. In this paper, we concentrate on privacy issue and propose a novel data encryption approach, named as Dynamic Data Encryption Strategy (D2ES). Our proposed approach aims to selectively encrypt data using privacy classification methods under timing constraints. This approach is designed to maximize the privacy protection scope by using a selective encryption strategy within the required execution time requirements. The performance of D2ES has been evaluated in our experiments, which provides the proof of the privacy enhancement.
@cite_36: Processing queries on encrypted data protects sensitive data stored in cloud databases. CryptDB has introduced the approach of adjustable encryption for such processing. A database column is adjusted to the necessary level of encryption, e.g. order-preserving, for the set of executed queries, but never reversed. This has the drawback that long running cloud databases will eventually transform into only order-preserving encrypted databases. In this paper we propose searchable encryption as an alternative in order to reduce this encryption degradation. It maintains security while only marginally impacting performance when applied only to infrequently used queries for searching. We present a budget-based encryption selection algorithm as part of query planning for making the appropriate choice between searchable and deterministic or order-preserving encryption. We evaluate our algorithm on a long-tail distributed TPC-C benchmark on an experimental implementation of encrypted queries in an in-memory database. In one choice of parameters our algorithm incurs only a (1.5 ) performance penalty, but one of 15 columns is not decrypted to order-preserving or deterministic encryption. Our selection algorithm is configurable, such that higher security gains are possible at the cost of performance.
@cite_28: 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.
@cite_29: 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-
@cite_56: 1. Surveying Sensitive Topics Asking Sensitive Questions Types of Errors in Surveys Ethical Issues in Surveys Legal Means of Privacy Protections Privacy Protection Through Anonymity Confidentiality and Privacy Protection Need for an Alternative Strategy 2. The Randomized Response Technique Warner's Randomized Response Design Unrelated Question Approach Forced Alternative Response Repeat Randomization 3. Design Considerations in Randomized Response Alternative Question Content Randomizing Devise and Question Selection Probability Distribution of Alternative Questions Reconciling Respondent Hazards and Efficiency 4. Alternatives to Randomized Response Non-Randomized Response Use of Lists and Counts 5. Extensions of Randomized Response Generalization to Polychotomous Measures Untruthful Respondents as a Special Group Extension to Quantitative Measures 6. Analyzing Randomized Response Data Measures of Association Randomized Response as Disclosure Control An Alternative Formulation Randomized Response Software 7. Applications of Randomized Response Methods Substantive Applications Comparison Studies Validation Research 8. Epilogue
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@cite_43: How to control the access of the huge amount of big data becomes a very challenging issue, especially when big data are stored in the cloud. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising encryption technique that enables end-users to encrypt their data under the access policies defined over some attributes of data consumers and only allows data consumers whose attributes satisfy the access policies to decrypt the data. In CP-ABE, the access policy is attached to the ciphertext in plaintext form, which may also leak some private information about end-users. Existing methods only partially hide the attribute values in the access policies, while the attribute names are still unprotected. In this paper, we propose an efficient and fine-grained big data access control scheme with privacy-preserving policy. Specifically, we hide the whole attribute (rather than only its values) in the access policies. To assist data decryption, we also design a novel attribute bloom filter to evaluate whether an attribute is in the access policy and locate the exact position in the access policy if it is in the access policy. Security analysis and performance evaluation show that our scheme can preserve the privacy from any linear secret-sharing schemes access policy without employing much overhead.
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@cite_50: Cloud computing is growing exponentially, whereby there are now hundreds of cloud service providers (CSPs) of various sizes. While the cloud consumers may enjoy cheaper data storage and computation offered in this multi-cloud environment, they are also in face of more complicated reliability issues and privacy preservation problems of their outsourced data. Though searchable encryption allows users to encrypt their stored data while preserving some search capabilities, few efforts have sought to consider the reliability of the searchable encrypted data outsourced to the clouds. In this paper, we propose a privacy-preserving STorage and REtrieval (STRE) mechanism that not only ensures security and privacy but also provides reliability guarantees for the outsourced searchable encrypted data. The STRE mechanism enables the cloud users to distribute and search their encrypted data across multiple independent clouds managed by different CSPs, and is robust even when a certain number of CSPs crash. Besides the reliability, STRE also offers the benefit of partially hidden search pattern. We evaluate the STRE mechanism on Amazon EC2 using a real world dataset and the results demonstrate both effectiveness and efficiency of our approach.
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@cite_23: With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the random space perturbation (RASP) data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries. We have carefully analyzed the attacks on data and queries under a precisely defined threat model and realistic security assumptions. Extensive experiments have been conducted to show the advantages of this approach on efficiency and security.
@cite_11: 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.
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 .