Data Perturbation Approach Research Articles

A hybrid data perturbation and mean clustering approach based privacy preserving classification model for large databases

A hybrid data perturbation and mean clustering approach based privacy preserving classification model for large databases

An efficient perturbation approach for multivariate data in sensitive and reliable data mining

Due to the rapid enhancement of technology, cloud data is increasing rapidly which contains individuals’ sensitive information such as medical diagnostics reports. While extracting knowledge from those sensitive data, both privacy of individuals’ and the utility of data should be preserved which is a crucial concern in data mining related activities. Though therein exist several methods to preserve privacy, a single method can not maintain the harmony interim privacy and data utility. Often achieving individuals’ privacy leads to the loss of the data utility and the opposite is true also. To address the vital issue, a four-stage data perturbation approach, called NRoReM, is proposed in this work based on normalization, geometric rotation, linear regression, and scalar multiplication for sensitive data mining. The proposed approach is experimented with over ten UCI data set using three benchmark classifiers. The empirical exploration of privacy protection, attack resistance, information entropy analysis, data utility, and error analysis exhibits that NRoReM preserves both privacy of individuals’ and data utility on a larger scale for 90% of the data set than 3-Dimensional Rotation Transformation (3DRT) and 2-Dimensional Rotation Transformation (2DRT).

PPaaS: Privacy Preservation as a Service

Personally identifiable information (PII) can find its way into cyberspace through various channels, and many potential sources can leak such information. Data sharing (e.g. cross-agency data sharing) for machine learning and analytics is one of the important components in data science. However, due to privacy concerns, data should be enforced with strong privacy guarantees before sharing. Different privacy-preserving approaches were developed for privacy preserving data sharing; however, identifying the best privacy-preservation approach for the privacy-preservation of a certain dataset is still a challenge. Different parameters can influence the efficacy of the process, such as the characteristics of the input dataset, the strength of the privacy-preservation approach, and the expected level of utility of the resulting dataset (on the corresponding data mining application such as classification). This paper presents a framework named Privacy Preservation as aService (PPaaS) to reduce this complexity. The proposed method employs selective privacy preservation via data perturbation and looks at different dynamics that can influence the quality of the privacy preservation of a dataset. PPaaS includes pools of data perturbation methods, and for each application and the input dataset, PPaaS selects the most suitable data perturbation approach after rigorous evaluation. It enhances the usability of privacy-preserving methods within its pool; it is a generic platform that can be used to sanitize big data in a granular, application-specific manner by employing a suitable combination of diverse privacy-preserving algorithms to provide a proper balance between privacy and utility.

Design of Retrievable Data Perturbation Approach and TPA for Public Cloud Data Security

In cloud computing, privacy is turn out to be the foremost difficulty. Therefore, this document is used to develop the cloud data defense by the help of an efficient data perturbation method. In the anticipated data perturbation method, the original data is recoverable whenever required. Additionally, the approach employs an enhanced cloud drops which is used to generate a noise among required mean and covariance in order get the actual data during retrieval. The privacy is preserved by means of Proxy Re-encryption algorithm on before the retrieval. During the outsourcing of data, data owner needs to ensure its security for the privacy purposes. Hereby, we have enabled a third party auditing to periodically check the perturbed data. This method is executed in the functioning platform of JAVA by way of ClouSim simulator.

A Combined Clustering and Geometric Data Perturbation Approach for Enriching Privacy Preservation of Healthcare Data in Hybrid Clouds

A Combined Clustering and Geometric Data Perturbation Approach for Enriching Privacy Preservation of Healthcare Data in Hybrid Clouds

A reversible data transform algorithm using integer transform for privacy-preserving data mining

In the cloud computing environment, since data owners worry about private information in their data being disclosed without permission, they try to retain the knowledge within the data, while applying privacy-preserving techniques to the data. In the past, a data perturbation approach was commonly used to modify the original data content, but it also results in data distortion, and hence leads to significant loss of knowledge within the data. To solve this problem, this study introduced the concept of reversible integer transformation in the image processing domain and developed a Reversible Data Transform (RDT) algorithm that can disrupt and restore data. In the RDT algorithm, using an adjustable weighting mechanism, the degree of data perturbation was adjusted to increase the flexibility of privacy-preserving. In addition, it allows the data to be embedded with a watermark, in order to identify whether the perturbed data has been tampered with. Experimental results show that, compared with the existing algorithms, RDT has better knowledge reservation and is better in terms of effectively reducing information loss and privacy disclosure risk. In addition, it has a high watermark payload.

An Efficient Hybrid Data Perturbation Approach to Preserve Privacy

Sharing of electronic data among organizations may disclose individual's sensitive information. Privacy preserving data mining (PPDM) plays a vital role in sensitive data protection. We propose an efficient hybrid data perturbation approach to address the privacy of both numeric and categorical attributes in electronic records. The categorical data are converted to numeric then original data are perturbed using S-based fuzzy membership function. Then attribute based multiplicative data perturbation is performed on different real world data sets to generate distorted data. Our proposed approach has proved its efficiency with K-means clustering and minimum distance classification of data before and after data transformation.

Tuple Value Based Multiplicative Data Perturbation Approach to Preserve Privacy in Data Stream Mining

Huge volume of data from domain specific applications such as medical, financial, library, telephone, shopping records and individual are regularly generated. Sharing of these data is proved to be beneficial for data mining application. On one hand such data is an important asset to business decision making by analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information for data analysis. In order to share data while preserving privacy, data owner must come up with a solution which achieves the dual goal of privacy preservation as well as an accuracy of data mining task - clustering and classification. An efficient and effective approach has been proposed that aims to protect privacy of sensitive information and obtaining data clustering with minimum information loss.

Ensemble member generation for sequential data assimilation

Using an ensemble of model forecasts to describe forecast error covariance extends linear sequential data assimilation schemes to nonlinear applications. This approach forms the basis of the Ensemble Kalman Filter and derivative filters such as the Ensemble Square Root Filter. While ensemble data assimilation approaches are commonly reported in the scientific literature, clear guidelines for effective ensemble member generation remain scarce. As the efficiency of the filter is reliant on the accurate determination of forecast error covariance from the ensemble, this paper describes an approach for the systematic determination of random error. Forecast error results from three factors: errors in initial condition, forcing data and model equations. The method outlined in this paper explicitly acknowledges each of these sources in the generation of an ensemble. The initial condition perturbation approach presented optimally spans the dynamic range of the model states and allows an appropriate ensemble size to be determined. The forcing data perturbation approach treats forcing observations differently according to their nature. While error from model physics is not dealt with in detail, discussion of some commonly used approaches and their limitations is provided. The paper concludes with an example application for a synthetic coastal hydrodynamic experiment assimilating sea surface temperature (SST) data, which shows better prediction capability when contrasted with standard approaches in the literature.

A Tree-Based Data Perturbation Approach for Privacy-Preserving Data Mining

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.

An Enhanced Data Perturbation Approach for Small Data Sets

ABSTRACTAs modern organizations gather, analyze, and share large quantities of data, issues of privacy, and confidentiality are becoming increasingly important. Perturbation methods are used to protect confidentiality when confidential, numerical data are shared or disseminated for analysis. Unfortunately, existing perturbation methods are not suitable for protecting small data sets. With small data sets, existing perturbation methods result in reduced protection against disclosure risk due to sampling error. Sampling error may also produce different results from the analysis of perturbed data compared to the original data, reducing data utility. In this study, we develop an enhancement of an existing perturbation technique, General Additive Data Perturbation, that can be used to effectively mask both large and small data sets. The proposed enhancement minimizes the risk of disclosure while ensuring that the results of commonly performed statistical analyses are identical and equal for both the original and the perturbed data.

Accessibility, Security, and Accuracy in Statistical Databases: The Case for the Multiplicative Fixed Data Perturbation Approach

Organizations store data regarding their operations, employees, consumers, and suppliers in their databases. Some of the data are considered confidential, and by law, the organization is required to provide appropriate security measures in order to preserve privacy. Yet a number of companies have little or no security measures. The reason for this lack of security may, at least in part, be attributed to a lack of awareness and empirical evidence about the relative effectiveness of security mechanisms. This study investigates the effectiveness of different security mechanisms for protecting numerical database attributes. The trade-off between security, accessibility, and accuracy are examined. A comparison of different security mechanisms reveals that fixed data perturbation is preferred because it maximizes both security and accessibility. An investigation of the different approaches to fixed data perturbation indicates that multiplicative method best meets these criteria.