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Kato Mivule

Bowie State University, Computer Science, Post-Doc

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Kato Mivule, D.Sc., is a researcher, currently as a National Research Council (NRC) Post Doctoral Research associate at the Air Force Institute of Technology. His research interests include data privacy engineering, cybersecurity, and data analytics via machine learning.

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Manuel David Masseno

Instituto Politécnico de Beja

University of Cologne

University of Massachusetts, Boston

Roshan Chitrakar

Nepal College of Information Technology

Graduate Center of the City University of New York

Kyrgyz-Turkish Manas University

LUISS Guido Carli

Praxis Business School

Viacheslav Kuleshov

Stockholm University

University of Technology Sydney

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Papers by Kato Mivule

An investigation of data privacy and utility using machine learning as a gauge

ProQuest LLC eBooks, 2014

Towards Agent-based Data Privacy Engineering

While a number of agent-based software engineering frameworks have been proposed in the recent ye... more While a number of agent-based software engineering frameworks have been proposed in the recent years, a few have been suggested specifically for the data privacy procedure. Yet still, one of the challenges in designing agent-based data privacy frameworks is that the very definition of privacy remains ambiguous and a case-by-case approach would have to be adopted. Therefore, as a contribution, we take a look at the literature on agent-based software engineering and present SIED (Specifications, Implementation, Evaluation, Dissemination), a conceptual framework that takes a holistic approach to the data privacy engineering process by looking at the Specifications, Implementation, Evaluation, and finally, Dissemination of the privatized datasets by autonomous intelligent agents.

A heuristic attack detection approach using the “least weighted” attributes for cyber security data

2017 Intelligent Systems Conference (IntelliSys)

The continuous advance in recent cloud-based computer networks has generated a number of security... more The continuous advance in recent cloud-based computer networks has generated a number of security challenges associated with intrusions in network systems. With the exponential increase in the volume of network traffic data, involvement of humans in such detection systems is time consuming and a non-trivial problem. Secondly, network traffic data tends to be highly dimensional, comprising of numerous features and attributes, making classification challenging and thus susceptible to the curse of dimensionality problem. Given such scenarios, the need arises for dimensional reduction, feature selection, combined with machine-learning techniques in the classification of such data. Therefore, as a contribution, this paper seeks to employ data mining techniques in a cloud-based environment, by selecting appropriate attributes and features with the least importance in terms of weight for the classification. Often the standard is to select features with better weights while ignoring those with least weights. In this study, we seek to find out if we can make prediction using those features with least weights. The motivation is that adversaries use stealth to hide their activities from the obvious. The question then is, can we predict any stealth activity of an adversary using the least observed attributes? In this particular study, we employ information gain to select attributes with the lowest weights and then apply machine learning to classify if a combination, in this case, of both source and destination ports are attacked or not. The motivation of this investigation is if attributes that are of least importance can be used to predict if an attack could occur. Our preliminary results show that even when the source and destination port attributes are used in combination with features with the least weights, it is possible to classify such network traffic data and predict if an attack will occur or not.

Distortion Search – A Web Search Privacy Heuristic

Search engines have vast technical capabilities to retain Internet search logs for each user and ... more Search engines have vast technical capabilities to retain Internet search logs for each user and thus present major privacy vulnerabilities to both individuals and organizations in revealing user intent. Additionally, many of the web search privacy enhancing tools available today require that the user trusts a third party, which make confidentiality of user intent even more challenging. The user is left at the mercy of the third party without the control over his or her own privacy. In this article, we suggest a user-centric heuristic, Distortion Search, a web search query privacy methodology that works by the formation of obfuscated search queries via the permutation of query keyword categories, and by strategically applying kanonymised web navigational clicks on URLs and Ads to generate a distorted user profile and thus providing specific user intent and query confidentiality. We provide empirical results via the evaluation of distorted web search queries in terms of retrieved sea...

Data Swapping for Private Information Sharing of Web Search Logs

Procedia Computer Science, 2017

With the increasing number of sophisticated cyber attacks on both government and private infrastr... more With the increasing number of sophisticated cyber attacks on both government and private infrastructure, cybersecurity data sharing is critical for the advancement of collaborative research among various entities, both in government, private sector, and academia. Of recent, the US Congress passed the Cyber Intelligence Sharing and Protection Act, as a framework for data sharing between various entities. Nevertheless this development raises the issue of trust between the collaborating parties, since shared data could be revealing. Conversely, due to the sensitive and confidential nature of the data involved, entities would have to employ various anonymization techniques to meet legal requirements in compliance with confidentiality policies of both their own organizations and federal government requirements. Secondly, a basic sharing of the data without the privatization process could make entities involved vulnerable to insider and inference attacks. For instance, an entity sharing data on cyber attacks might accidently reveal a sensitive network topology to an untrusted collaborator. As a contribution, we propose a modest but effective data privacy enhancement heuristic; a targeted 2k basic data swapping of individual web search log records. In this heuristic, if individual has a set of x records in their web search log set A, those records are swapped in that individual set A, then swapped again with another individual y records in set B. Our preliminary results show that data swapping is effective for big data and it would be demanding to trace the original issuer of the queries in a given large dataset of web search logs, thus providing some level of confidentiality.

Permutation of Web Search Query Types for User Intent Privacy

International Journal of Advanced Computer Science and Applications, 2017

Privacy remains a major concern when using search engines to find for information on the web due ... more Privacy remains a major concern when using search engines to find for information on the web due to the fact that search engines own massive resources in preserving search logs of each user and organizations. However, many of the present query search privacy practices require the very same search engine and third party to collaborate, making privacy even more difficult. Therefore, as a contribution, we present a heuristic, permutation of web search query types, a noncryptographic heuristic that works by formation of obfuscated search queries via permutation of query keyword categories. Preliminary results from this study show that web search query and specific user intent privacy might be achievable from the user side without involvement of the search engine or other third parties by the permutation of web search query types.

Web Search Query Privacy, an End-User Perspective

Journal of Information Security, 2017

While search engines have become vital tools for searching information on the Internet, privacy i... more While search engines have become vital tools for searching information on the Internet, privacy issues remain a growing concern due to the technological abilities of search engines to retain user search logs. Although such capabilities might provide enhanced personalized search results, the confidentiality of user intent remains uncertain. Even with web search query obfuscation techniques, another challenge remains, namely, reusing the same obfuscation methods is problematic, given that search engines have enormous computation and storage resources for query disambiguation. A number of web search query privacy procedures involve the cooperation of the search engine, a nontrusted entity in such cases, making query obfuscation even more challenging. In this study, we provide a review on how search engines work in regards to web search queries and user intent. Secondly, this study reviews material in a manner accessible to those outside computer science with the intent to introduce knowledge of web search engines to enable non-computer scientists to approach web search query privacy innovatively. As a contribution, we identify and highlight areas open for further investigative and innovative research in regards to end-user personalized web search privacy-that is methods that can be executed on the user side without third party involvement such as, search engines. The goal is to motivate future web search obfuscation heuristics that give users control over their personal search privacy.

Towards Agent-based Data Privacy Engineering

While a number of agent-based software engineering frameworks have been proposed in the recent ye... more While a number of agent-based software engineering frameworks have been proposed in the recent years, a few have been suggested specifically for the data privacy procedure. Yet still, one of the challenges in designing agent-based data privacy frameworks is that the very definition of privacy remains ambiguous and a case-by-case approach would have to be adopted. Therefore, as a contribution, we take a look at the literature on agent-based software engineering and present SIED (Specifications, Implementation, Evaluation, Dissemination), a conceptual framework that takes a holistic approach to the data privacy engineering process by looking at the Specifications, Implementation, Evaluation, and finally, Dissemination of the privatized datasets by autonomous intelligent agents.

SIED, a Data Privacy Engineering Framework

While a number of data privacy techniques have been proposed in the recent years, a few framework... more While a number of data privacy techniques have been proposed in the recent years, a few frameworks have been suggested for the implementation of the data privacy process. Most of the proposed approaches are tailored towards implementing a specific data privacy algorithm but not the overall data privacy engineering and design process. Therefore, as a contribution, this study proposes SIED (Specification, Implementation, Evaluation, and Dissemination), a conceptual framework that takes a holistic approach to the data privacy engineering procedure by looking at the specifications, implementation, evaluation, and finally, dissemination of the privatized datasets.

A Review of CUDA, MapReduce, and Pthreads Parallel Computing Models

The advent of high performance computing (HPC) and graphics processing units (GPU), present an en... more The advent of high performance computing (HPC) and graphics processing units (GPU), present an enormous computation resource for Large data transactions (big data) that require parallel processing for robust and prompt data analysis. While a number of HPC frameworks have been proposed, parallel programming models present a number of challenges-for instance, how to fully utilize features in the different programming models to implement and manage parallelism via multi-threading in both CPUs and GPUs. In this paper, we take an overview of three parallel programming models, CUDA, MapReduce, and Pthreads. The goal is to explore literature on the subject and provide a high level view of the features presented in the programming models to assist high performance users with a concise understanding of parallel programming concepts and thus faster implementation of big data projects using high performance computing.

A Study of Privatized Synthetic Data Generation Using Discrete Cosine Transforms

International Journal of Advanced Computer Science and Applications, 2014

In order to comply with data confidentiality requirements, while meeting usability needs for rese... more In order to comply with data confidentiality requirements, while meeting usability needs for researchers, entities are faced with the challenge of how to publish privatized data sets that preserve the statistical traits of the original data. One solution to this problem, is the generation of privatized synthetic data sets. However, during data privatization process, the usefulness of data, have a propensity to diminish even as privacy might be guaranteed. Furthermore, researchers have documented that finding an equilibrium between privacy and utility is intractable, often requiring trade-offs. Therefore, as a contribution, the Filtered Classification Error Gauge heuristic, is presented. The suggested heuristic is a data privacy and usability model that employs data privacy, signal processing, and machine learning techniques to generate privatized synthetic data sets with acceptable levels of usability. Preliminary results from this study show that it might be possible to generate privacy compliant synthetic data sets using a combination of data privacy, signal processing, and machine learning techniques, while preserving acceptable levels of data usability.

A Codon Frequency Obfuscation Heuristic for Raw Genomic Data Privacy

Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology, 2015

Genomic data provides clinical researchers with vast opportunities to study various patient ailme... more Genomic data provides clinical researchers with vast opportunities to study various patient ailments. Yet the same data contains revealing information, some of which a patient might want to remain concealed. The question then arises: how can an entity transact in full DNA data while concealing certain sensitive pieces of information in the genome sequence, and maintain DNA data utility? As a response to this question, we propose a codon frequency obfuscation heuristic, in which a redistribution of codon frequency values with highly expressed genes is done in the same amino acid group, generating an obfuscated DNA sequence. Our preliminary results show that it might be possible to publish an obfuscated DNA sequence with a desired level of similarity (utility) to the original DNA sequence.

A study of usability-aware network trace anonymization

2015 Science and Information Conference (SAI), 2015

The publication and sharing of network trace data is a critical to the advancement of collaborati... more The publication and sharing of network trace data is a critical to the advancement of collaborative research among various entities, both in government, private sector, and academia. However, due to the sensitive and confidential nature of the data involved, entities have to employ various anonymization techniques to meet legal requirements in compliance with confidentiality policies. Nevertheless, the very composition of network trace data makes it a challenge when applying anonymization techniques. On the other hand, basic application of microdata anonymization techniques on network traces is problematic and does not deliver the necessary data usability. Therefore, as a contribution, we point out some of the ongoing challenges in the network trace anonymization. We then suggest usability-aware anonymization heuristics by employing microdata privacy techniques while giving consideration to usability of the anonymized data. Our preliminary results show that with trade-offs, it might be possible to generate anonymized network traces with enhanced usability, on a case-by-case basis using micro-data anonymization techniques.

An Investigation of Data Privacy and Utility Preservation Using KNN Classification as a Gauge

It is obligatory that organizations by law safeguard the privacy of individuals when handling dat... more It is obligatory that organizations by law safeguard the privacy of individuals when handling datasets containing personal identifiable information (PII). Nevertheless, during the process of data privatization, the utility or usefulness of the privatized data diminishes. Yet achieving the optimal balance between data privacy and utility needs has been documented as an NP-hard challenge. In this study, we investigate data privacy and utility preservation using KNN machine learning classification as a gauge.

An Investigation of Data Privacy and Utility Using Machine Learning Classification Methods as a Gauge (Thesis Abstract)

A Review of Privacy Essentials for Confidential Mobile Data Transactions

The increasingly rapid use of mobile devices for data transaction around the world has consequent... more The increasingly rapid use of mobile devices for data transaction around the world has consequently led to a new problem, and that is, how to engage in mobile data transactions while maintaining an acceptable level of data privacy and security. While most mobile devices engage in data transactions through a data cloud or a set of data servers, it is still possible to apply data confidentiality across data servers, and, as such, preserving privacy in any mobile data transaction. Yet still, it is essential that a review of data privacy, data utility, the techniques, and methodologies employed in the data privacy process, is done, as the underlying data privacy principles remain the same. In this paper, as a contribution, we present a review of data privacy essentials that are fundamental in delivering any appropriate analysis and specific methodology implementation for various data privacy needs in mobile data transactions and computation.

On the Generation of Privatized Synthetic Data Using Distance Transforms

Organizations have interest in research collaboration efforts that involve data sharing with peer... more Organizations have interest in research collaboration efforts that involve data sharing with peers. However, such partnerships often come with confidentiality risks that could involve insider attacks and untrustworthy collaborators who might leak sensitive information. To mitigate such data sharing vulnerabilities, entities share privatized data with retracted sensitive information. However, while such data sets might offer some assurances of privacy, maintaining the statistical traits of the original data, is often problematic, leading to poor data usability. Therefore, in this paper, a confidential synthetic data generation heuristic, that employs a combination of data privacy and distance transforms techniques, is presented. The heuristic is used for the generation of privatized numeric synthetic data, while preserving the statistical traits of the original data. Empirical results from applying unsupervised learning, using k-means, to test the usability of the privatized synthetic data set, are presented. Preliminary results from this implementation show that it might be possible to generate privatized synthetic data sets, with the same statistical morphological structure as the original, using data privacy and distance transforms methods.

Utilizing Noise Addition for Data Privacy, an Overview

The internet is increasingly becoming a standard for both the production and consumption of data ... more The internet is increasingly becoming a standard for both the production and consumption of data while at the same time cyber-crime involving the theft of private data is growing. Therefore in efforts to securely transact in data, privacy and security concerns must be taken into account to ensure that the confidentiality of individuals and entities involved is not compromised, and that the data published is compliant to privacy laws. In this paper, we take a look at noise addition as one of the data privacy providing techniques. Our endeavor in this overview is to give a foundational perspective on noise addition data privacy techniques, provide statistical consideration for noise addition techniques and look at the current state of the art in the field, while outlining future areas of research.

An Overview of Data Privacy in Multi-Agent Learning Systems

Public and private sector entities continuously produce, store, and transact in large amounts of ... more Public and private sector entities continuously produce, store, and transact in large amounts of data. However, combined with the growth of the internet, such datasets get stored and accessed on multiple devices, locations, and across the globe. Therefore, the necessity for autonomous agents that can learn across distributed systems to extract knowledge from large datasets while at the same time taking into account data privacy considerations while interacting with other agents remains a challenge. In this paper, we endeavor to provide an overview of data privacy in multi-agent learning systems, while at the same time highlighting current challenges and future areas of work and research.

Applying Data Privacy Techniques on Published Data in Uganda

ABSTRACT

An investigation of data privacy and utility using machine learning as a gauge

ProQuest LLC eBooks, 2014

Towards Agent-based Data Privacy Engineering

While a number of agent-based software engineering frameworks have been proposed in the recent ye... more While a number of agent-based software engineering frameworks have been proposed in the recent years, a few have been suggested specifically for the data privacy procedure. Yet still, one of the challenges in designing agent-based data privacy frameworks is that the very definition of privacy remains ambiguous and a case-by-case approach would have to be adopted. Therefore, as a contribution, we take a look at the literature on agent-based software engineering and present SIED (Specifications, Implementation, Evaluation, Dissemination), a conceptual framework that takes a holistic approach to the data privacy engineering process by looking at the Specifications, Implementation, Evaluation, and finally, Dissemination of the privatized datasets by autonomous intelligent agents.

A heuristic attack detection approach using the “least weighted” attributes for cyber security data

2017 Intelligent Systems Conference (IntelliSys)

The continuous advance in recent cloud-based computer networks has generated a number of security... more The continuous advance in recent cloud-based computer networks has generated a number of security challenges associated with intrusions in network systems. With the exponential increase in the volume of network traffic data, involvement of humans in such detection systems is time consuming and a non-trivial problem. Secondly, network traffic data tends to be highly dimensional, comprising of numerous features and attributes, making classification challenging and thus susceptible to the curse of dimensionality problem. Given such scenarios, the need arises for dimensional reduction, feature selection, combined with machine-learning techniques in the classification of such data. Therefore, as a contribution, this paper seeks to employ data mining techniques in a cloud-based environment, by selecting appropriate attributes and features with the least importance in terms of weight for the classification. Often the standard is to select features with better weights while ignoring those with least weights. In this study, we seek to find out if we can make prediction using those features with least weights. The motivation is that adversaries use stealth to hide their activities from the obvious. The question then is, can we predict any stealth activity of an adversary using the least observed attributes? In this particular study, we employ information gain to select attributes with the lowest weights and then apply machine learning to classify if a combination, in this case, of both source and destination ports are attacked or not. The motivation of this investigation is if attributes that are of least importance can be used to predict if an attack could occur. Our preliminary results show that even when the source and destination port attributes are used in combination with features with the least weights, it is possible to classify such network traffic data and predict if an attack will occur or not.

Distortion Search – A Web Search Privacy Heuristic

Search engines have vast technical capabilities to retain Internet search logs for each user and ... more Search engines have vast technical capabilities to retain Internet search logs for each user and thus present major privacy vulnerabilities to both individuals and organizations in revealing user intent. Additionally, many of the web search privacy enhancing tools available today require that the user trusts a third party, which make confidentiality of user intent even more challenging. The user is left at the mercy of the third party without the control over his or her own privacy. In this article, we suggest a user-centric heuristic, Distortion Search, a web search query privacy methodology that works by the formation of obfuscated search queries via the permutation of query keyword categories, and by strategically applying kanonymised web navigational clicks on URLs and Ads to generate a distorted user profile and thus providing specific user intent and query confidentiality. We provide empirical results via the evaluation of distorted web search queries in terms of retrieved sea...

Data Swapping for Private Information Sharing of Web Search Logs

Procedia Computer Science, 2017

With the increasing number of sophisticated cyber attacks on both government and private infrastr... more With the increasing number of sophisticated cyber attacks on both government and private infrastructure, cybersecurity data sharing is critical for the advancement of collaborative research among various entities, both in government, private sector, and academia. Of recent, the US Congress passed the Cyber Intelligence Sharing and Protection Act, as a framework for data sharing between various entities. Nevertheless this development raises the issue of trust between the collaborating parties, since shared data could be revealing. Conversely, due to the sensitive and confidential nature of the data involved, entities would have to employ various anonymization techniques to meet legal requirements in compliance with confidentiality policies of both their own organizations and federal government requirements. Secondly, a basic sharing of the data without the privatization process could make entities involved vulnerable to insider and inference attacks. For instance, an entity sharing data on cyber attacks might accidently reveal a sensitive network topology to an untrusted collaborator. As a contribution, we propose a modest but effective data privacy enhancement heuristic; a targeted 2k basic data swapping of individual web search log records. In this heuristic, if individual has a set of x records in their web search log set A, those records are swapped in that individual set A, then swapped again with another individual y records in set B. Our preliminary results show that data swapping is effective for big data and it would be demanding to trace the original issuer of the queries in a given large dataset of web search logs, thus providing some level of confidentiality.

Permutation of Web Search Query Types for User Intent Privacy

International Journal of Advanced Computer Science and Applications, 2017

Privacy remains a major concern when using search engines to find for information on the web due ... more Privacy remains a major concern when using search engines to find for information on the web due to the fact that search engines own massive resources in preserving search logs of each user and organizations. However, many of the present query search privacy practices require the very same search engine and third party to collaborate, making privacy even more difficult. Therefore, as a contribution, we present a heuristic, permutation of web search query types, a noncryptographic heuristic that works by formation of obfuscated search queries via permutation of query keyword categories. Preliminary results from this study show that web search query and specific user intent privacy might be achievable from the user side without involvement of the search engine or other third parties by the permutation of web search query types.

Web Search Query Privacy, an End-User Perspective

Journal of Information Security, 2017

While search engines have become vital tools for searching information on the Internet, privacy i... more While search engines have become vital tools for searching information on the Internet, privacy issues remain a growing concern due to the technological abilities of search engines to retain user search logs. Although such capabilities might provide enhanced personalized search results, the confidentiality of user intent remains uncertain. Even with web search query obfuscation techniques, another challenge remains, namely, reusing the same obfuscation methods is problematic, given that search engines have enormous computation and storage resources for query disambiguation. A number of web search query privacy procedures involve the cooperation of the search engine, a nontrusted entity in such cases, making query obfuscation even more challenging. In this study, we provide a review on how search engines work in regards to web search queries and user intent. Secondly, this study reviews material in a manner accessible to those outside computer science with the intent to introduce knowledge of web search engines to enable non-computer scientists to approach web search query privacy innovatively. As a contribution, we identify and highlight areas open for further investigative and innovative research in regards to end-user personalized web search privacy-that is methods that can be executed on the user side without third party involvement such as, search engines. The goal is to motivate future web search obfuscation heuristics that give users control over their personal search privacy.

Towards Agent-based Data Privacy Engineering

While a number of agent-based software engineering frameworks have been proposed in the recent ye... more While a number of agent-based software engineering frameworks have been proposed in the recent years, a few have been suggested specifically for the data privacy procedure. Yet still, one of the challenges in designing agent-based data privacy frameworks is that the very definition of privacy remains ambiguous and a case-by-case approach would have to be adopted. Therefore, as a contribution, we take a look at the literature on agent-based software engineering and present SIED (Specifications, Implementation, Evaluation, Dissemination), a conceptual framework that takes a holistic approach to the data privacy engineering process by looking at the Specifications, Implementation, Evaluation, and finally, Dissemination of the privatized datasets by autonomous intelligent agents.

SIED, a Data Privacy Engineering Framework

While a number of data privacy techniques have been proposed in the recent years, a few framework... more While a number of data privacy techniques have been proposed in the recent years, a few frameworks have been suggested for the implementation of the data privacy process. Most of the proposed approaches are tailored towards implementing a specific data privacy algorithm but not the overall data privacy engineering and design process. Therefore, as a contribution, this study proposes SIED (Specification, Implementation, Evaluation, and Dissemination), a conceptual framework that takes a holistic approach to the data privacy engineering procedure by looking at the specifications, implementation, evaluation, and finally, dissemination of the privatized datasets.

A Review of CUDA, MapReduce, and Pthreads Parallel Computing Models

The advent of high performance computing (HPC) and graphics processing units (GPU), present an en... more The advent of high performance computing (HPC) and graphics processing units (GPU), present an enormous computation resource for Large data transactions (big data) that require parallel processing for robust and prompt data analysis. While a number of HPC frameworks have been proposed, parallel programming models present a number of challenges-for instance, how to fully utilize features in the different programming models to implement and manage parallelism via multi-threading in both CPUs and GPUs. In this paper, we take an overview of three parallel programming models, CUDA, MapReduce, and Pthreads. The goal is to explore literature on the subject and provide a high level view of the features presented in the programming models to assist high performance users with a concise understanding of parallel programming concepts and thus faster implementation of big data projects using high performance computing.

A Study of Privatized Synthetic Data Generation Using Discrete Cosine Transforms

International Journal of Advanced Computer Science and Applications, 2014

In order to comply with data confidentiality requirements, while meeting usability needs for rese... more In order to comply with data confidentiality requirements, while meeting usability needs for researchers, entities are faced with the challenge of how to publish privatized data sets that preserve the statistical traits of the original data. One solution to this problem, is the generation of privatized synthetic data sets. However, during data privatization process, the usefulness of data, have a propensity to diminish even as privacy might be guaranteed. Furthermore, researchers have documented that finding an equilibrium between privacy and utility is intractable, often requiring trade-offs. Therefore, as a contribution, the Filtered Classification Error Gauge heuristic, is presented. The suggested heuristic is a data privacy and usability model that employs data privacy, signal processing, and machine learning techniques to generate privatized synthetic data sets with acceptable levels of usability. Preliminary results from this study show that it might be possible to generate privacy compliant synthetic data sets using a combination of data privacy, signal processing, and machine learning techniques, while preserving acceptable levels of data usability.

A Codon Frequency Obfuscation Heuristic for Raw Genomic Data Privacy

Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology, 2015

Genomic data provides clinical researchers with vast opportunities to study various patient ailme... more Genomic data provides clinical researchers with vast opportunities to study various patient ailments. Yet the same data contains revealing information, some of which a patient might want to remain concealed. The question then arises: how can an entity transact in full DNA data while concealing certain sensitive pieces of information in the genome sequence, and maintain DNA data utility? As a response to this question, we propose a codon frequency obfuscation heuristic, in which a redistribution of codon frequency values with highly expressed genes is done in the same amino acid group, generating an obfuscated DNA sequence. Our preliminary results show that it might be possible to publish an obfuscated DNA sequence with a desired level of similarity (utility) to the original DNA sequence.

A study of usability-aware network trace anonymization

2015 Science and Information Conference (SAI), 2015

The publication and sharing of network trace data is a critical to the advancement of collaborati... more The publication and sharing of network trace data is a critical to the advancement of collaborative research among various entities, both in government, private sector, and academia. However, due to the sensitive and confidential nature of the data involved, entities have to employ various anonymization techniques to meet legal requirements in compliance with confidentiality policies. Nevertheless, the very composition of network trace data makes it a challenge when applying anonymization techniques. On the other hand, basic application of microdata anonymization techniques on network traces is problematic and does not deliver the necessary data usability. Therefore, as a contribution, we point out some of the ongoing challenges in the network trace anonymization. We then suggest usability-aware anonymization heuristics by employing microdata privacy techniques while giving consideration to usability of the anonymized data. Our preliminary results show that with trade-offs, it might be possible to generate anonymized network traces with enhanced usability, on a case-by-case basis using micro-data anonymization techniques.

An Investigation of Data Privacy and Utility Preservation Using KNN Classification as a Gauge

It is obligatory that organizations by law safeguard the privacy of individuals when handling dat... more It is obligatory that organizations by law safeguard the privacy of individuals when handling datasets containing personal identifiable information (PII). Nevertheless, during the process of data privatization, the utility or usefulness of the privatized data diminishes. Yet achieving the optimal balance between data privacy and utility needs has been documented as an NP-hard challenge. In this study, we investigate data privacy and utility preservation using KNN machine learning classification as a gauge.

An Investigation of Data Privacy and Utility Using Machine Learning Classification Methods as a Gauge (Thesis Abstract)

A Review of Privacy Essentials for Confidential Mobile Data Transactions

The increasingly rapid use of mobile devices for data transaction around the world has consequent... more The increasingly rapid use of mobile devices for data transaction around the world has consequently led to a new problem, and that is, how to engage in mobile data transactions while maintaining an acceptable level of data privacy and security. While most mobile devices engage in data transactions through a data cloud or a set of data servers, it is still possible to apply data confidentiality across data servers, and, as such, preserving privacy in any mobile data transaction. Yet still, it is essential that a review of data privacy, data utility, the techniques, and methodologies employed in the data privacy process, is done, as the underlying data privacy principles remain the same. In this paper, as a contribution, we present a review of data privacy essentials that are fundamental in delivering any appropriate analysis and specific methodology implementation for various data privacy needs in mobile data transactions and computation.

On the Generation of Privatized Synthetic Data Using Distance Transforms

Organizations have interest in research collaboration efforts that involve data sharing with peer... more Organizations have interest in research collaboration efforts that involve data sharing with peers. However, such partnerships often come with confidentiality risks that could involve insider attacks and untrustworthy collaborators who might leak sensitive information. To mitigate such data sharing vulnerabilities, entities share privatized data with retracted sensitive information. However, while such data sets might offer some assurances of privacy, maintaining the statistical traits of the original data, is often problematic, leading to poor data usability. Therefore, in this paper, a confidential synthetic data generation heuristic, that employs a combination of data privacy and distance transforms techniques, is presented. The heuristic is used for the generation of privatized numeric synthetic data, while preserving the statistical traits of the original data. Empirical results from applying unsupervised learning, using k-means, to test the usability of the privatized synthetic data set, are presented. Preliminary results from this implementation show that it might be possible to generate privatized synthetic data sets, with the same statistical morphological structure as the original, using data privacy and distance transforms methods.

Utilizing Noise Addition for Data Privacy, an Overview

The internet is increasingly becoming a standard for both the production and consumption of data ... more The internet is increasingly becoming a standard for both the production and consumption of data while at the same time cyber-crime involving the theft of private data is growing. Therefore in efforts to securely transact in data, privacy and security concerns must be taken into account to ensure that the confidentiality of individuals and entities involved is not compromised, and that the data published is compliant to privacy laws. In this paper, we take a look at noise addition as one of the data privacy providing techniques. Our endeavor in this overview is to give a foundational perspective on noise addition data privacy techniques, provide statistical consideration for noise addition techniques and look at the current state of the art in the field, while outlining future areas of research.

An Overview of Data Privacy in Multi-Agent Learning Systems

Public and private sector entities continuously produce, store, and transact in large amounts of ... more Public and private sector entities continuously produce, store, and transact in large amounts of data. However, combined with the growth of the internet, such datasets get stored and accessed on multiple devices, locations, and across the globe. Therefore, the necessity for autonomous agents that can learn across distributed systems to extract knowledge from large datasets while at the same time taking into account data privacy considerations while interacting with other agents remains a challenge. In this paper, we endeavor to provide an overview of data privacy in multi-agent learning systems, while at the same time highlighting current challenges and future areas of work and research.

Applying Data Privacy Techniques on Published Data in Uganda

ABSTRACT

Lit Review by Kato Mivule: A Review of Genetic Algorithms

Lit Review Talk by Kato Mivule: Protecting DNA Sequence Anonymity with Generalization Lattices (Malin, 2005)