Privacy-preserving incremental data dissemination

Proceedings of the 2007 ACM symposium on Applied computing - SAC '07, 2007

New privacy regulations together with ever increasing data availability and computational power have created a huge interest in data privacy research. One major research direction is built around k-anonymity property, which is required for the released data. Although many k-anonymization algorithms exist for static data, a complete framework to cope with data evolution (a real world scenario) has not been proposed before. In this paper, we introduce algorithms for the maintenance of k-anonymized versions of large evolving datasets. These algorithms incrementally manage insert/delete/update dataset modifications. Our results showed that incremental maintenance is very efficient compared with existing techniques and preserves data quality. The second main contribution of this paper is an optimization algorithm that is able to improve the quality of the solutions attained by either the non-incremental or incremental algorithms.

In this decade, Massive amount of digital information has created the gateway for gathering knowledge and effective decision-making. Private organizations and government sectors publish the data for business, statistical analysis and research purposes. While publishing the data that represents the individuals' information, for sharing to public, Privacy preservation should be followed. The objective of privacy preservation protects the individual's sensitive information from adversaries, during data publishing. Researchers have contributed the anonymization methods and algorithms for publishing the static dataset. Data collection process execute in different time stamp. Sometimes released information has updated in different time stamp and published in different time series. Hence, Anonymization techniques for static dataset turn into worthless. Although existing methods in the literature perform the anonymization, they do not take care of increasing information loss and ignoring the privacy of data owners because of complexity of process. In addition, Increasing information loss of anonymized dataset twist the data become meaningless. In this paper, Our Proposed method preserves the individual privacy in incremental dataset publishing scenario and also reduce the information loss through grouping record-sets based on relational algebra and applies local re coding technique. Adult dataset with more than 30,000 records are experimented with the proposed method. The results show the significant reduction in information loss over existing methods.

Classification is a fundamental problem in data analysis. Training a classifier requires accessing a large collection of data. Releasing person-specific data, such as customer data or patient records, may pose a threat to individual's privacy. Even after removing explicit identifying information such as Name and SSN, it is still possible to link released records back to their identities by matching some combination of non-identifying attributes such as {Sex,Zip, Birth date}. A useful approach to combat such linking attacks, called k-anonymization , is anonymizing the linking attributes so that at least k released records match each value combination of the linking attributes. Previous work attempted to find an optimal k-anonymization that minimizes some data distortion metric. We argue that minimizing the distortion to the training data is not relevant to the classification goal that requires extracting the structure of predication on the " future " data. In this paper, we propose a anonymization solution for classification. Our goal is to find a anonymization, not necessarily optimal in the sense of minimizing data distortion, that preserves the classification structure. We conducted intensive experiments to evaluate the impact of anonymization on the classification on future data. Experiments on real life data show that the quality of classification can be preserved even for highly restrictive anonymity requirements.

International Journal of Information and Computer Security, 2013

The emerging of the internet-based services poses a privacy threat to the individuals. Data transformation to meet a privacy standard becomes a requirement for typical data processing for the services. (k, e)-anonymisation is one of the most promising data transformation approaches, since it can provide high-accuracy aggregate query results. Though, the computational cost of the algorithm providing optimal solutions for such approach is not very high, i.e., O(n 2). In certain environments, the data to be processed can be appended at any time. In this paper, we address an efficiency issue of the incremental privacy preservation using (k, e)-anonymisation approach. The impact of the increment is observed theoretically. We propose an incremental algorithm based on such observation. The algorithm can replace the quadratic-complexity processing by a linear function on some part of the dataset, while the optimal results are guaranteed. Additionally, a few indexes are proposed to further improve the efficiency of the proposed algorithm. The experiments have been conducted to validate our work. From the results, it can be seen that the proposed work is highly efficient comparing with the non-incremental algorithm and an approximation algorithm.

Information Sciences, 2014

We study the problem of privacy preservation in sequential releases of databases. In that scenario, several releases of the same table are published over a period of time, where each release contains a different set of the table attributes, as dictated by the purposes of the release. The goal is to protect the private information from adversaries who examine the entire sequential release. That scenario was studied in and was further investigated in . We revisit their privacy definitions, and suggest a significantly stronger adversarial assumption and privacy definition. We then present a sequential anonymization algorithm that achieves -diversity. The algorithm exploits the fact that different releases may include different attributes in order to reduce the information loss that the anonymization entails. Unlike the previous algorithms, ours is perfectly scalable as the runtime to compute the anonymization of each release is independent of the number of previous releases. In addition, we consider here the fully dynamic setting in which the different releases differ in the set of attributes as well as in the set of tuples. The advantages of our approach are demonstrated by extensive experimentation.

2009 IEEE International Conference on Data Mining Workshops, 2009

The k-anonymization method is a commonly used privacy-preserving technique. Previous studies used various measures of utility that aim at enhancing the correlation between the original public data and the generalized public data. We, bearing in mind that a primary goal in releasing the anonymized database for data mining is to deduce methods of predicting the private data from the public data, propose a new information-theoretic measure that aims at enhancing the correlation between the generalized public data and the private data. Such a measure significantly enhances the utility of the released anonymized database for data mining. We then proceed to describe a new and highly efficient algorithm that is designed to achieve k-anonymity with high utility. That algorithm is based on a modified version of sequential clustering which is the method of choice in clustering, and it is independent of the underlying measure of utility.

Transactions on Emerging Telecommunications Technologies, 2020

In a real-world scenario for privacy-preserving data publishing, the original data is anonymized and released periodically. Each release may vary in number of records due to insert, update, and delete operations. An intruder can combine i.e. correlate different releases to compromise the privacy of the individual records. Most of the literature, such as-safety,-safe (l, k)-diversity, have an inconsistency in record signatures and adds counterfeit tuples with high generalization that causes privacy breach and information loss. In this paper, we propose an improved privacy model (,)-slicedBucket, having a novel idea of "Cache" table to address these limitations. We indicate that a collusion attack can be performed for breaching the privacy of-safe (l, k)-diversity privacy model, and demonstrate it through formal modeling. The objective of the proposed (,)-slicedBucket privacy model is to set a tradeoff between strong privacy and enhanced utility. Furthermore, we formally model and analyze the proposed model to show that the collusion attack is no longer applicable. Extensive experiments reveal that the proposed approach outperforms the existing models.

Very Large Data Bases, 2007

Recent research studied the problem of publishing microdata without revealing sensitive information, leading to the pri- vacy preserving paradigms of k-anonymity and `-diversity. k-anonymity protects against the identification of an indi- vidual's record. `-diversity, in addition, safeguards against the association of an individual with specific sensitive infor- mation. However, existing approaches suffer from at least one of the following drawbacks:

Information Security in Diverse Computing Environments

Streaming data emerges from different electronic sources and needs to be processed in real time with minimal delay. Data streams can generate hidden and useful knowledge patterns when mined and analyzed. In spite of these benefits, the issue of privacy needs to be addressed before streaming data is released for mining and analysis purposes. In order to address data privacy concerns, several techniques have emerged. K-anonymity has received considerable attention over other privacy preserving techniques because of its simplicity and efficiency in protecting data. Yet, k-anonymity cannot be directly applied on continuous data (data streams) because of its transient nature. In this chapter, the authors discuss the challenges faced by k-anonymity algorithms in enforcing privacy on data streams and review existing privacy techniques for handling data streams.

2015 IEEE International Conference on Big Data (Big Data), 2015

Among the privacy-preserving approaches that are known in the literature, k-anonymity remains the basis of more advanced models while still being useful as a stand-alone solution. Applying k-anonymity in practice, though, incurs severe loss of data utility, thus limiting its effectiveness and reliability in real-life applications and systems. However, such loss in utility does not necessarily arise from an inherent drawback of the model itself, but rather from the deficiencies of the algorithms used to implement the model.Conventional approaches rely on a methodology that publishes data in homogeneous generalized groups. An alternative modern data publishing scheme focuses on publishing the data in heterogeneous groups and achieves higher utility, while ensuring the same privacy guarantees. As conventional approaches cannot anonymize data following this heterogeneous scheme, innovative solutions are required for this purpose. Following this approach, in this paper we provide a set of algorithms that ensure high-utility k-anonymity, via solving an equivalent graph processing problem.