Alina Câmpan

Northern Kentucky University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Traian Marius Truţă

Northern Kentucky University

GȘ

Gabriela Șerban

Babeș-Bolyai University

Temesguen Messay-Kebede

Wright-Patterson Air Force Base

John Musgrave

College of Wooster

David Kapp

Wright-Patterson Air Force Base

Anca Ralescu

University of Cincinnati

Nicholas Cooper

Rensselaer Polytechnic Institute

Matthew Beckerich

Northern Kentucky University

Yasmeen Alufaisan

Prince Mohammad bin Fahd University

John H. McB. Miller

Cooperative Institutions

Northern Kentucky University

Highland Community College - Illinois

Babeș-Bolyai University

University of Cincinnati

Purdue University West Lafayette

Wright-Patterson Air Force Base

Southwest Jiaotong University

SUNY New Paltz

Technical University of Cluj-Napoca

United States Air Force Research Laboratory

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

On-the-Fly Hierarchies for Numerical Attributes in Data Anonymization

Lecture notes in computer science (2010)

Alina Câmpan Nicholas Cooper

Microdata (statistics)

k-Anonymity

Data anonymization

Closeness

On the fly

Hierarchical clustering

Information loss

10.1007/978-3-642-15546-8_2

Cite

Citations (4)

K -anonymization incremental maintenance and optimization techniques

Traian Marius Truţă Alina Câmpan

New privacy regulations together with ever increasing data availability and computational power have created a huge interest in data privacy research. One major research direction is built around k-anonymity property, which is required for the released data. Although many k-anonymization algorithms exist for static data, a complete framework to cope with data evolution (a real world scenario) has not been proposed before. In this paper, we introduce algorithms for the maintenance of k-anonymized versions of large evolving datasets. These algorithms incrementally manage insert/delete/update dataset modifications. Our results showed that incremental maintenance is very efficient compared with existing techniques and preserves data quality. The second main contribution of this paper is an optimization algorithm that is able to improve the quality of the solutions attained by either the non-incremental or incremental algorithms.

k-Anonymity

Data anonymization

10.1145/1244002.1244093

Cite

Citations (43)

Efficient Approximation Algorithms for Minimum Dominating Sets in Social Networks

International Journal of Service Science Management Engineering and Technology (2018)

Traian Marius Truţă Alina Câmpan Matthew Beckerich

Social networks are increasingly becoming an outlet that is more and more powerful in spreading news and influence individuals. Compared with other traditional media outlets such as newspaper, radio, and television, social networks empower users to spread their ideological message and/or to deliver target advertising very efficiently in terms of both cost and time. In this article, the authors focus on efficiently finding dominating sets in social networks for the classical dominating set problem as well as for two related problems: partial dominating sets and d-hop dominating sets. They will present algorithms for determining efficiently a good approximation for the social network minimum dominating sets for each of the three variants. The authors ran an extensive suite of experiments to test the presented algorithms on several datasets that include real networks made available by the Stanford Network Analysis Project and synthetic networks that follow the power-law and random models that they generated for this work. The performed experiments show that the selection of the algorithm that performs best to determine efficiently the dominating set is dependent of network characteristics and the order of importance between the size of the dominating set and the time required to determine such a set.

Dominating set

Social network (sociolinguistics)

10.4018/ijssmet.2018040101

Cite

Citations (10)

User-controlled generalization boundaries for p -sensitive k -anonymity

Alina Câmpan Traian Marius Truţă Nicholas Cooper

Numerous privacy models based on the k-anonymity property have been introduced in the last few years. While differing in their methods and quality of their results, they all focus first on masking the data, and then protecting the quality of the data as a whole. We consider a new approach, where requirements on the amount of distortion allowed on the initial data are imposed in order to preserve its usefulness. In this paper, the constrained p-sensitive k-anonymity model is introduced and an algorithm for generating constrained p-sensitive k-anonymous microdata is presented. Our experiments have shown that the proposed algorithm is comparable quality-wise with existing algorithms.

k-Anonymity

Microdata (statistics)

10.1145/1774088.1774317

Cite

Citations (5)

Generating Microdata with P-Sensitive K-Anonymity Property

Lecture notes in computer science (2007)

Traian Marius Truţă Alina Câmpan Paul Meyer

Microdata (statistics)

k-Anonymity

Closeness

Data anonymization

10.1007/978-3-540-75248-6_9

Cite

Citations (37)

Avoiding Attribute Disclosure with the (Extended) p-Sensitive k-Anonymity Model

Annals of information systems (2009)

Traian Marius Truţă Alina Câmpan

Microdata (statistics)

k-Anonymity

Closeness

10.1007/978-1-4419-1280-0_16

Cite

Citations (7)

Fast Dominating Set Algorithms for Social Networks.

MAICS (2015)

Alina Câmpan Traian Marius Truţă Matthew Beckerich

In this paper we introduce two novel algorithms that are able to efficiently determine an approximation to the minimum dominating set problem, and at the same time, they will preserve the quality of the solution to an acceptable level. We compare these two algorithms with three existing algorithms, for a large number of synthetic datasets, and for several real world social networks. For experiments, we use social network generators that create both power-law and random networks, and a few real network datasets made available by the Stanford Network Analysis Project. Our experiments show that the proposed algorithms are viable, and in many instances, preferable for determining the minimum dominating set of a social network.

Social network (sociolinguistics)

Dominating set

Social Network Analysis

Source

Cite

Citations (11)