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In the era of big data analytics, data owner is more concern about the data privacy. Data anonymization approaches such as k -anonymity, l -diversity, and t -closeness are used for a long time to preserve privacy in published data. However, these approaches cannot be directly applicable to a large amount of data. Distributed programming framework such as MapReduce and Spark are used for big data analytics which add more challenges to privacy preserving data publishing. Recently, we identified few scalable approaches for Privacy Preserving Big Data Publishing in literature and majority of them are based on k -anonymity and l -diversity. However, these approaches require a significant improvement to reach the level of existing privacy preserving data publishing approaches, therefore, we propose Improved Scalable l- Diversity (ImSLD) approach which is the extension of Improved Scalable k -Anonymity (ImSKA) for scalable anonymization in this paper. Our approaches are based on scalable k -anonymization that uses MapReduce as a programming paradigm. We use poker dataset and synthesize big data versions of poker dataset to test our approaches. The result analysis shows significant improvement in terms of running time due to the lesser number of MapReduce iterations and also exhibits lower information loss as compared to existing approaches while providing the same level of privacy due to tight arrangement of the records in the initial equivalence class. [ABSTRACT FROM AUTHOR]

Abstract The trends of data mining in the healthcare is increased due to the digitization of healthcare with electronic health record (EHR) systems. This generates a huge amount of data on daily basis. Data mining with the healthcare data has given the new direction to medical research for early detection of diseases and improving patient care. Many data mining applications require the integration of data from the different sources. For example, the integration of outpatient medical records and health examination data helps to identify the correlation between abnormal test result and disease. The result of association rule mining on this integrated data helps to build the knowledge center for disease prevention, which facilitate the healthcare provider in follow up treatment and prevention. The integration of data requires the sharing of sensitive information about the patients. Disclosing the sensitive information violates the privacy of patients. In this paper, we tackle the problem of privacy preserving association rule mining in vertically partition healthcare data. Furthermore, we analyze the proposed approach in terms of privacy preservation, communication and computation cost. [ABSTRACT FROM AUTHOR]

Big data analytics has created opportunities for researchers to process huge amount of data but created a big threat to privacy of individual. Data processed by big data analytics platforms may have personal information which need to be taken care of when deriving some useful results for research. Existing privacy preserving techniques like, anonymization requires having dataset divided in the set of attributes like, sensitive attributes, quasi identifiers, and non-sensitive attributes. With the structured data it may possible to have such a distribution but in unstructured data it is very difficult to identify sensitive attribute and quasi identifiers. [ABSTRACT FROM AUTHOR]

The immense popularity of social networks has rendered them vulnerable to security threats such as Sybil attacks. In Sybil attacks, attackers send several friend requests from fake identities, and some of the genuine users might accept them unknowingly and become victims of Sybil attacks. Once genuine users accept friend requests from Sybil identities, attackers can spam, phish, and conduct other harmful actions in the target networks. Though genuine users have links to Sybil users unknowingly, they do less interaction and have low strength of relationship with them. Thus, the relationship strength measure can be useful to detect a Sybil attack. Considering the relationship strength measure, we propose a behavior-based and graph-based hybrid approach to detect a Sybil attack in Online Social Networks (OSNs). We identify the behavior features, use them to measure the relationship strength, and identify attack edges. We use the graph-based feature(betweenness-centrality) to leverage attack-edge identification and detect Sybil nodes. We have evaluated our scheme using real-world datasets, and experimental results validate the proposed scheme's effectiveness. [Display omitted] • A hybrid scheme is suggested to detect Sybil attacks in online social networks. • We identify and apply behavior-based important features to identify attack edges. • An important graph-based structural feature is employed to detect Sybil nodes. • The proposed scheme is effective and outperforms current approaches. [ABSTRACT FROM AUTHOR]

Abstract: With the widespread popularity of Internet, the use of databases has increased tremendously and theft of the database is a main concern for the database owners. Therefore, it is essential to protect the ownership of the database. In this paper, a new technique of database watermarking is proposed which based on inserting the bits of a binary image in relational database. The proposed technique also minimizes the variation in watermarked database. Experimental results justify the feasibility of the proposed technique and its robustness against common database attacks. [Copyright &y& Elsevier]