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Nawal Muhammed Almutairi

Assistant Professor

Assistant Professor

علوم الحاسب والمعلومات
المدينة الجامعية للطالبات بالدرعية - 6T86
Conference Paper

Data Clustering Using Homomorphic Encryption and Secure Chain Distance Matrices


Secure data mining has emerged as an essential requirement for exchanging confidential data in terms of third party (outsourced) data analytics. An emerging form of encryption, Homomorphic Encryption, allows a limited amount of data manipulation and, when coupled with additional information, can facilitate secure third party data analytics. However, the resource required is substantial which leads to scalability issues. Moreover, in many cases, data owner participation can still be significant, thus not providing a full realisation of the vision of third party data analytics. The focus of this paper is therefore scalable and secure third party data clustering with only very limited data owner participation. To this end, the concept of Secure Chain Distance Matrices is proposed. The mechanism is fully described and analysed in the context of three different clustering algorithms. Excellent evaluation results were obtained.

more of publication

The paper introduces the Secure kNN (SkNN) approach to data classification and querying. The approach is founded on the concept of Secure Chain Distance Matrices (SCDMs) whereby the classification…

by Nawal Almutairi, Frans Coenen, Keith Dures

With the advances in machine learning techniques and the potency of cloud computing there is an increasing adoption of third party cloud services for outsourcing training and prediction of machine…


To study the variation in emotional responses to stimuli, different methods have been developed to elicit emotions in a replicable way. Using video clips has been shown to be the most effective…

by Nawal Al-Mutairi, Sharifa Alghowinem, Areej Al-Wabil