The topic of big data has attracted increasing interest in recent years. The emergence of big data leads to new difficulties in terms of protection models used for data privacy, which is of necessity for sharing and processing data. Protecting individuals’ sensitive information while maintaining the usability of the data set published is the most important challenge in privacy preserving. In this regard, data anonymization methods are utilized in order to protect data against identity disclosure and linking attacks. In this study, a novel data anonymization algorithm based on chaos and perturbation has been proposed for privacy and utility preserving in big data. The performance of the proposed algorithm is evaluated in terms of Kullback–Leibler divergence, probabilistic anonymity, classification accuracy, F-measure and execution time. The experimental results have shown that the proposed algorithm is efficient and performs better in terms of Kullback–Leibler divergence, classification accuracy and F-measure compared to most of the existing algorithms using the same data set. Resulting from applying chaos to perturb data, such successful algorithm is promising to be used in privacy preserving data mining and data publishing.

In this paper, both a sliding mode controller (SMC) and proportional-integral-derivative (PID) controller are designed for a multi-degrees-of-freedom structure, which has an active mass damper (AMD) to suppress earthquakeor wind-induced vibration. Since the model might have uncertainties and/or parameter changes, a SMC has been included because of its robust character and performance. The structural system has five degrees of freedom and has been simulated against an initial displacement of the first floor. At the end of the paper, we present the time histories of the first floor, top floor, and AMD displacements, the control voltage and frequency response of the uncontrolled, PID controlled, and sliding mode controlled structures, and we discuss the results.

Büyük verinin ortaya çıkışı bilgi güvenliği ve klasik güvenlik tedbirleri için kullanılan koruma modelleri için yeni zorluklara neden olmaktadır. Bu çalışmada büyük veri güvenliği ve büyük veride kişi mahremiyetinin korunmasına yönelik literatürde var olan çalışmalar özetlenmiştir. Buna ek olarak büyük veri kaynaklarının neler olduğu, büyük veri sistemlerini korumak için gerekli olan araçlar ve bu sistemlerin güvenliğinin sağlanmasında karşılaşılan zorluklar açıklanmıştır.