FLAIM: A Multi-level Anonymization Framework for Computer and Network Logs

FLAIM (Framework for Log Anonymization and Information Management) addresses two important needs not well addressed by current log anonymizers. First, it is extremely modular and not tied to the specific log being anonymized. Second, it supports multi-level anonymization, allowing system administrators to make fine-grained trade-offs between information loss and privacy/security concerns. In this paper, we examine anonymization solutions to date and note the above limitations in each. We further describe how FLAIM addresses these problems, and we describe FLAIM's architecture and features in detail.Comment: 16 pages, 4 figures, in submission to USENIX Lis

Privacy Preservation by Disassociation

In this work, we focus on protection against identity disclosure in the publication of sparse multidimensional data. Existing multidimensional anonymization techniquesa) protect the privacy of users either by altering the set of quasi-identifiers of the original data (e.g., by generalization or suppression) or by adding noise (e.g., using differential privacy) and/or (b) assume a clear distinction between sensitive and non-sensitive information and sever the possible linkage. In many real world applications the above techniques are not applicable. For instance, consider web search query logs. Suppressing or generalizing anonymization methods would remove the most valuable information in the dataset: the original query terms. Additionally, web search query logs contain millions of query terms which cannot be categorized as sensitive or non-sensitive since a term may be sensitive for a user and non-sensitive for another. Motivated by this observation, we propose an anonymization technique termed disassociation that preserves the original terms but hides the fact that two or more different terms appear in the same record. We protect the users' privacy by disassociating record terms that participate in identifying combinations. This way the adversary cannot associate with high probability a record with a rare combination of terms. To the best of our knowledge, our proposal is the first to employ such a technique to provide protection against identity disclosure. We propose an anonymization algorithm based on our approach and evaluate its performance on real and synthetic datasets, comparing it against other state-of-the-art methods based on generalization and differential privacy.Comment: VLDB201

A Hybrid Multi-user Cloud Access Control based Block Chain Framework for Privacy Preserving Distributed Databases

Most of the traditional medical applications are insecure and difficult to compute the data integrity with variable hash size. Traditional medical data security systems are insecure and it depend on static parameters for data security. Also, distributed based cloud storage systems are independent of integrity computational and data security due to unstructured data and computational memory. As the size of the data and its dimensions are increasing in the public and private cloud servers, it is difficult to provide the machine learning based privacy preserving in cloud computing environment. Block-chain technology plays a vital role for large cloud databases. Most of the conventional block-chain frameworks are based on the existing integrity and confidentiality models. Also, these models are based on the data size and file format. In this model, a novel integrity verification and encryption framework is designed and implemented in cloud environment.  In order to overcome these problems in the cloud computing environment, a hybrid integrity and security-based block-chain framework is designed and implemented on the large distributed databases. In this framework,a novel decision tree classifier is used along with non-linear mathematical hash algorithm and advanced attribute-based encryption models are used to improve the privacy of multiple users on the large cloud datasets. Experimental results proved that the proposed advanced privacy preserving based block-chain technology has better efficiency than the traditional block-chain based privacy preserving systems on large distributed databases

Differential Private Data Collection and Analysis Based on Randomized Multiple Dummies for Untrusted Mobile Crowdsensing

Mobile crowdsensing, which collects environmental information from mobile phone users, is growing in popularity. These data can be used by companies for marketing surveys or decision making. However, collecting sensing data from other users may violate their privacy. Moreover, the data aggregator and/or the participants of crowdsensing may be untrusted entities. Recent studies have proposed randomized response schemes for anonymized data collection. This kind of data collection can analyze the sensing data of users statistically without precise information about other users\u27 sensing results. However, traditional randomized response schemes and their extensions require a large number of samples to achieve proper estimation. In this paper, we propose a new anonymized data-collection scheme that can estimate data distributions more accurately. Using simulations with synthetic and real datasets, we prove that our proposed method can reduce the mean squared error and the JS divergence by more than 85% as compared with other existing studies

BIG DATA ANALYTICS - AN OVERVIEW

   Big Data Analytics has been in advance more attention recently since researchers in business and academic world are trying to successfully mine and use all possible knowledge from the vast amount of data generated and obtained. Demanding a paradigm shift in the storage, processing and analysis of Big Data, traditional data analysis methods stumble upon large amounts of data in a short period of time. Because of its importance, the U.S. Many agencies, including the government, have in recent years released large funds for research in Big Data and related fields. This gives a concise summary of investigate growth in various areas related to big data processing and analysis and terminate with a discussion of research guidelines in the similar areas. &nbsp