Articulation Point Based Quasi Identifier Detection for Privacy Preserving in Distributed Environment

These days, huge data size requires high-end resources to be stored in IT organizations premises. They depend on cloud for additional resource necessities. Since cloud is a third-party, we cannot guarantee high security for our information as it might be misused. This necessitates the need of privacy in data before sharing to the cloud. Numerous specialists proposed several methods, wherein they attempt to discover explicit identifiers and sensitive data before distributing it. But, quasi-identifiers are attributes which can spill data of explicit identifiers utilizing background knowledge. Analysts proposed strategies to find quasi- identifiers with the goal that these properties can likewise be considered for implementing privacy. But, these techniques suffer from many drawbacks like higher time consumption and extract more quasi identifiers which decreases data utility. The proposed work overcomes this drawback by extracting minimum required quasi attributes with minimum time complexity

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

ρ-uncertainty Anonymization by Partial Suppression

Abstract. We present a novel framework for set-valued data anonymiza-tion by partial suppression regardless of the amount of background knowl-edge the attacker possesses, and can be adapted to both space-time and quality-time trade-offs in a “pay-as-you-go ” approach. While minimizing the number of item deletions, the framework attempts to either preserve the original data distribution or retain mineable useful association rules, which targets statistical analysis and association mining, two major data mining applications on set-valued data.

DIGITAL IDENTITY MODELLING FOR DIGITAL FINANCIAL SERVICES IN ZAMBIA

Identification and verification have always been at the heart of financial services and payments, which is even more the case in the digital age. So, while banks have long been trusted to keep money safe, is there a new role for them as stewards of digital identity? Governments should, in consultation with the private sector, develop a national identity strategy based on a federated-style model in which public and private sector identity providers would compete to supply trusted digital identities to individuals and businesses. Back then, when the world seemed smaller, slower and more local, physical identity documents were adequate for face-to-face transactions. However, the Internet changed everything. It shrank distances, created new business models and generally sped everything up. From the innovation lifecycle to access to information, processes and the clock-speed on risk, the Internet has accelerated everything. The use of Internet in doing business has grown over the years in Africa and Zambia in particular. As such, the incidences of online identity theft have grown too. Identity theft is becoming a prevalent and increasing problem in Zambia. An identity thief only requires certain identity information to decimate a victim's life and credit. This research proposes to identify and extract various forms of identity attributes from various sources used in the physical and cyberspace to identity users accessing the financial services through extracting identity attributes from the various forms of identity credentials and application forms. Finally, design a digital identity model based on Shannon’s Information theory and Euclidean metric based Euclidean Distance Geometry (EDG) to be used for quantifying, implementation and validating of extracted identity attributes from various forms of identity credentials and application forms, in an effective way