• Corpus ID: 11833951

BIG DATA SECURITY AND PRIVACY Sponsored by the National Science Foundation September 16-17 , 2014 The University of Texas at Dallas

  title={BIG DATA SECURITY AND PRIVACY Sponsored by the National Science Foundation September 16-17 , 2014 The University of Texas at Dallas},
  author={Bhavani M. Thuraisingham and Elisa Bertino and Murat Kantarcioglu and Louis Beecherl},
This report describes the issues surrounding big data security and privacy and provides a summary of the National Science Foundation sponsored workshop on this topic held in Dallas, Texas on September 1617, 2014. Our goal is to build a community in big data security and privacy to explore the challenging research problems. 



Data mining, national security, privacy and civil liberties

The threats to privacy that can occur through data mining are described and the privacy problem is viewed as a variation of the inference problem in databases.

Security with Privacy-Opportunities and Challenges

There are opportunities and challenges concerning how to achieve security while still ensuring privacy and a number of questions that have been debated by the panel.

Privacy Preserving Data Mining

This work considers a scenario in which two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information, and proposes a protocol that is considerably more efficient than generic solutions and demands both very few rounds of communication and reasonable bandwidth.

Efficient privacy-aware record integration

A novel model for practical PRL is introduced, which affords controlled and limited information leakage, avoids false matches resulting from data transformation, and enables efficiency and privacy.

Privacy preserving schema and data matching

A protocol for record matching that preserves privacy both at the data level and at the schema level is proposed, and by running the protocol two sources can compute the matching of their datasets without sharing their data in clear and only sharing the result of the matching.

Privacy of outsourced k-means clustering

This paper presents a method that allows the data owner to encrypt its data with a homomorphic encryption scheme and the service provider to perform k-means clustering directly over the encrypted data.

Data Protection from Insider Threats

  • E. Bertino
  • Computer Science
    Data Protection from Insider Threats
  • 2012
This book discusses several techniques that can provide effective protection against attacks posed by people working on the inside of an organization, and introduces the notion of insider threat and reports some data about data breaches due to insider threats.

A Hybrid Approach to Private Record Linkage

This paper proposes a method that combines these two approaches and enables users to trade off between privacy, accuracy and cost and yields much more accurate matching results compared to sanitization techniques, even when the data sets are perturbed extensively.

A Hybrid Approach to Private Record Matching

This work proposes a hybrid technique that operates over sanitized data to filter out in a privacy-preserving manner pairs of records that do not satisfy the matching condition and provides a formal definition of privacy.

Vigiles: Fine-Grained Access Control for MapReduce Systems

This paper demonstrates how a broad class of safety policies, including fine-grained access control policies at the level of key-value data pairs rather than files, can be elegantly enforced on MapReduce clouds with minimal overhead and without any change to the system or OS implementations.