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 th...

In our recent paper “A simple tool for neuroimaging data sharing,” we introduced a system for sharing DICOM data. Addressing anonymization, we mentioned DICOM Supplement 551, the National Cancer Institute deidentification profile, and the default deidentification profile in XNAT’s DICOM Browser and noted the disagreement in these various anonymization profiles. While a careful analysis of anony...

Fill-in-the-bubble forms are widely used for surveys, election ballots, and standardized tests. In these and other scenarios, use of the forms comes with an implicit assumption that individuals’ bubble markings themselves are not identifying. This work challenges this assumption, demonstrating that fill-in-the-bubble forms could convey a respondent’s identity even in the absence of explicit ide...

Graphs that evolve over time are called temporal graphs. They can be used to describe and represent real-world networks, including transportation networks, social networks, and communication networks, with higher fidelity and accuracy. However, research is still limited on how to manage large scale temporal graphs and execute queries over these graphs efficiently and effectively. This thesis in...

Community structure is an important property that captures inhomogeneities common in large networks, and modularity is one of the most widely used metrics for such community structure. In this paper, we introduce a principled methodology, the Spectral Graph Forge, for generating random graphs that preserves community structure from a real network of interest, in terms of modularity. Our approac...

Data collected nowadays by social-networking applications create fascinating opportunities for building novel services, as well as expanding our understanding about social structures and their dynamics. Unfortunately, publishing socialnetwork graphs is considered an ill-advised practice due to privacy concerns. To alleviate this problem, several anonymization methods have been proposed, aiming ...

In recent years, the use of personal data in marketing, scientific and medical investigation, forecasting future trends has really increased. This information is used by government, companies, individuals, should not contain any sensitive that allows identification an individual. Therefore, anonymization essential nowadays. Data changes original to make it difficult identify ARX Anonymization A...

Graphs that evolve over time are called temporal graphs. They can be used to describe and represent real-world networks, including transportation networks, social networks, and communication networks, with higher fidelity and accuracy. However, research is still limited on how to manage large scale temporal graphs and execute queries over these graphs efficiently and effectively. This thesis in...

We present a unified metric for analyzing the risk of disclosing anonymized datasets. Datasets containing privacy sensitive information are often required to be shared with unauthorized users for utilization of valuable statistical properties of the data. Anonymizing the actual data provides a great opportunity to share the data while preserving its statistical properties and privacy. The risk ...

Recent research on de-anonymizing datasets of anonymized personal records has not deterred organizations from releasing personal data, often with ingenuous attempts at defeating de-anonymization. Studying such techniques provides scientific evidence as to why anonymization of high dimensional databases is hard and throws light on what kinds of techniques to avoid. We study how to de-anonymize d...