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53
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Introduction
Traian Marius Truta is a full professor of Computer Science at Northern Kentucky University (NKU) and currently serves as Interim School Director of the School of Computing and Analytics. He holds a Ph.D. in computer science degree from Wayne State University in 2004 and a M.S. and B.S. degrees both in Computer Science from “Babes-Bolyai University, Romania.
His major areas of research expertise are social networks, social media, data privacy and anonymity, privacy in statistical databases, an
Current institution
Publications
Publications (53)
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 bo...
FIFA World Cup is one of the most watched sporting event in the world and its popularity continue to increase. While currently 32 teams participate in this event, starting with 2026 the number of participants will increase to 48. As a result, FIFA proposed a major format change, the groups of 4 teams will be replaced by groups of 3 teams, with the...
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 bo...
Social networks such as Facebook and LinkedIn have gained a lot of popularity in recent years. These networks use a large amount of data that are highly valuable for different purposes. Hence, social networks become a potential vector for attackers to exploit. This chapter focuses on the security attacks and countermeasures used by social networks....
Social media and social networks are embedded in our society to a point that could not have been imagined only ten years ago. Facebook, LinkedIn, and Twitter are already well known social networks that have a large audience in all age groups. The amount of data that those social sites gather from their users is continually increasing and this data...
The seventh Workshop on Privacy and Anonymity in Information Society (PAIS 2014) was held in conjunction with the International Conference on Extending Database Technology (EDBT) and International Conference on Database Theory (ICDT) in Athens, Greece. The PAIS 2014 workshop provided an open yet focused platform for researchers and practitioners fr...
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...
Social media and social networks are embedded in our society to a point that could not have been imagined only ten years ago. Facebook, LinkedIn, and Twitter are already well known social networks that have a large audience in all age groups. Recently more trendy social sites such as Pinterest, Instagram, Vine, Tumblr, WhatsApp, and Snapchat are be...
Security is one of the biggest concerns of any company that has an IT infrastructure. Windows event logs are a very useful source of data for security information, but sometimes can be nearly impossible to use due to the complexity of log data or the number of events generated per minute. For this reason, event log data must be automatically proces...
In this paper, we present an overview of p-sensitive k-anonymity models including the basic model, the extended p-sensitive k-anonymity, the constrained p-sensitive k-anonymity, and the (p+, α)-sensitive k-anonymity. Existing properties of these models are reviewed and illustrated, and new properties regarding the maximum number of QI-clusters are...
During the similarity join process, one or more sources may not allow sharing its data with other sources. In this case, a privacy preserving similarity join is required. We showed in our previous work [4] that using long attributes, such as paper abstracts, movie summaries, product descriptions, and user feedbacks, could improve the similarity joi...
Social networks such as Facebook, LinkedIn, or Twitter have nowadays a global reach that surpassed all previous expectations. Many social networks gather confidential information of their users, and as a result, the privacy in social networks has become a topic of general interest. To defend against privacy violations, several social network anonym...
Extracting useful knowledge from social network datasets is a challenging problem. To add to the difficulty of this problem, privacy concerns that exist for many social network datasets have restricted the ability to analyze these networks and consequently to maximize the knowledge that can be extracted from them. This paper addresses this issue by...
Information brings economic value to the customers and data is the "soul" of the enterprise. Data centers are playing more and more important roles in the enterprises. Storage technology is one of the fundamental technologies behind data centers. The storage knowledge and skills are needed for data center professionals. Thus, we have developed a ne...
Generalization hierarchies are frequently used in computer science, statistics, biology, bioinformatics, and other areas when
less specific values are needed for data analysis. Generalization is also one of the most used disclosure control technique
for anonymizing data. For numerical attributes, generalization is performed either by using existing...
During the similarity join process, one or more sources may not allow sharing the whole data with other sources. In this case, privacy preserved similarity join is required. We showed in our previous work [4] that using long attributes, such as paper abstracts, movie summaries, product descriptions, and user feedbacks, could improve the similarity...
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 t...
Existing privacy regulations together with large amounts of available data created a huge interest in data privacy research.
A main research direction is built around the k-anonymity property. Several shortcomings of the k-anonymity model were addressed by new privacy models such as p-sensitive k-anonymity, l-diversity, (α,k)-anonymity, t-closeness...
Numerous privacy models based on the k-anonymity property and extending the k-anonymity model have been introduced in the last few years in data privacy research: l-diversity, p-sensitive k-anonymity, (α, k) anonymity, t-closeness, etc. While differing in their methods and quality of their results, they all focus first on masking the data, and then...
The Workshop on Privacy and Anonymity in the Information Society (PAIS 2008) held on March 29, 2008 was the first in its series with an aim to provide a focused platform for researchers and practitioners from computer science in privacy areas as statistics. The Workshop program included a keynote speech and eight paper presentations which was divid...
Publishing data for analysis from a microdata table containing sensitive attributes, while maintaining individual privacy, is a problem of increasing significance today. The k-anonymity model was proposed for privacy preserving data publication. While focusing on identity disclosure, k-anonymity model fails to protect attribute disclosure to some e...
Publishing data for analysis from a micro data table containing sensitive attributes, while maintaining individual privacy, is a problem of increasing significance today. The k-anonymity model was proposed for privacy preserving data publication. While focusing on identity disclosure, k-anonymity model fails to protect attribute disclosure to some...
Professionals in the field of speech technology are often constrained by a lack of speech corpora that are important to their research and development activities. These corpora exist within the archives of various businesses and institutions; however, these entities are often prevented from sharing their data due to privacy rules and regulations. E...
The advent of social network sites in the last years seems to be a trend that will likely continue. What naive technology
users may not realize is that the information they provide online is stored and may be used for various purposes. Researchers
have pointed out for some time the privacy implications of massive data gathering, and effort has been...
Existing privacy regulations together with large amounts of available data have created a huge interest in data privacy research. A main research direction is built around the k-anonymity property. Several shortcomings of the k-anonymity model have been fixed by new privacy models such as p-sensitive k-anonymity, l-diversity, (α, k)-anonymity, and...
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 cop...
In this paper, we introduce a new privacy protection property called p-sensitive k-anonymity. The existing kanonymity property protects against identity disclosure, but it fails to protect against attribute disclosure. The new introduced privacy model avoids this shortcoming. Two necessary conditions to achieve p-sensitive kanonymity property are p...
Association rule mining techniques are used to search attribute-value pairs that occur frequently together in a data set. Ordinal association rules are a particular type of association rules that describe orderings between attributes that commonly occur over a data set (9). Although ordinal association rules are defined between any number of the at...
In this paper, we introduce three global disclosure risk measures (minimal, maximal and weighted) for microdata with continuous
attributes. We classify the attributes of a given set of microdata in two different ways: based on its potential identification
utility and based on the order relation that exists in its domain of value. We define inversio...
In this paper we introduce a new privacy protection property, called extended p-sensitive k-anonymity, which is an extension of the p-sensitive k-anonymity property [16]. The new property is aware of confidential attributes hierarchies and of the existence of protected not ground-level confidential attributes values, situation not considered by pre...
In this paper, we introduce a general framework for microdata and three disclosure risk measures (minimal, maximal and weighted). We classify the attributes from a given microdata in two different ways: based on their potential identification utility and based on the order relation that exists in their domain of value. We define inversion and chang...
In this paper, we introduce three microdata disclosure risk measures (minimal, maximal and weighted) for sampling disclosure control method. The minimal disclosure risk measure represents the percentage of records that can be correctly identified by an intruder based on prior knowledge of key attribute values. The maximal disclosure risk measure co...
Governmental, public, and private institutions that systematically release data are increasingly concerned with possible misuses of their data that might lead to disclosure of confidential information. Moreover, confidentiality regulation requires that privacy of individuals represented in the released data must be protected. ^ In this dissertation...
In this paper, we first introduce minimal, maximal and weighted disclosure risk measures for microaggregation disclosure control method. Our disclosure risk measures are more applicable to real-life situations, compute the overall disclosure risk, and are not linked to a target individual. After defining those disclosure risk measures, we then intr...
We define several disclosure risk measures for microdata. We analyze disclosure risk based on the disclosure control techniques applied to initial microdata. Disclosure Control is the discipline concerned with the modification of data containing confidential information about individual entities, such as persons, households, businesses, etc. in ord...
The advent of social network sites in the last few years seems to be a trend that will likely continue in the years to come. Online social interaction has become very popular around the globe and most sociologists agree that this will not fade away. Such a development is possible due to the advancements in computer power, technologies, and the spre...
Disclosure Control is the discipline concerned with the modification of data containing confidential information about individual entities, such as persons, households, businesses, etc. in order to prevent third parties working with these data from recognizing entities in the data and thereby disclosing information about these entities. In very bro...
