Browsing by Author "Jafari, Nazanin"

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Open Access
An inference attack on genomic data using kinship, complex correlations, and phenotype information
(IEEE, 2018) Deznabi, Iman; Mobayen, Mohammad; Jafari, Nazanin; Taştan, Öznur; Ayday, Erman
Abstract—Individuals (and their family members) share (partial) genomic data on public platforms. However, using special characteristics of genomic data, background knowledge that can be obtained from the Web, and family relationship between the individuals, it is possible to infer the hidden parts of shared (and unshared) genomes. Existing work in this field considers simple correlations in the genome (as well as Mendel’s law and partial genomes of a victim and his family members). In this paper, we improve the existing work on inference attacks on genomic privacy. We mainly consider complex correlations in the genome by using an observable Markov model and recombination model between the haplotypes. We also utilize the phenotype information about the victims. We propose an efficient message passing algorithm to consider all aforementioned background information for the inference. We show that the proposed framework improves inference with significantly less information compared to existing work.
Open Access
Parallel streaming graph partitioning utilizing multilevel framework
(2018-08) Jafari, Nazanin
Graph partitioning is widely used for e cient parallelization of a variety of applications. Streaming graph partitioning is a one pass partitioning solution provided to overcome high computation costs of o ine graph partitioners. Even though these streaming algorithms can be used for successively repartitioning, aiming at further improvements in partitioning qualities, quality improvements is limited to few passes that make o ine graph partitioning tools still a desirable solution for graph partitioning due to the generated high quality partitions. We propose a multilevel approach using streaming algorithms that can alleviate tradeo between quality and performance in graph partitioning problem. Moreover, our OpenMP based multi-threaded implementation, can generate fast and highly scalable solutions compared to mt-metis, a multi-threaded solution for METIS, the state-of-the-art o ine high quality graph partitioning tool. Our results show that our method can produce up to fteen times faster and more scalable results in large graph datasets. We also show that our method can improve quality of partitions signi cantly compared to state-of-the-art streaming graph partitioning algorithm LDG after repartitioning several times. On average we produce partitions with 29% better qualities than LDG algorithm.