Ph.D. Theses
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Browsing Ph.D. Theses by Subject "Bayesian field theory."
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Item Bayesian approaches for privacy preserving data sharing(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2020., 2020.) Ermiş, Beyza.; Cemgil, Ali Taylan.In this thesis, we focus on the data fusion problem where we have heterogeneous data which is collected from di↵erent sources and stored in the form of matrices and higher-order tensors and propose coupled matrix and tensor factorization models to be able to jointly analyze these relational datasets. This method performs simulta neous factorization of matrices and tensors by extracting the common latent factors from the shared modes. We develop coupled models using various tensor models and cost functions for the missing link prediction problem and report the successful empir ical results. Most of the time, the data matrices and tensors are distributed between several parties. Sharing information across those parties brings the privacy protec tion requirement, therefore the second problem we handle is protecting the privacy of distributed and heterogeneous datasets. We exploit the connection between di?erential privacy and sampling from a Bayesian posterior to derive an efficient coupled tensor factorization algorithm. We empirically show that our methods are able to provide good prediction accuracy on synthetic and real datasets while providing provable pri vacy guarantee. Finally, we propose an approach to preserve the privacy of the neural network’s training data due to the connection between tensor factorization and neural networks. We introduce a dropout technique that provides an elegant Bayesian in terpretation to dropout, and show that the intrinsic noise added can be exploited to obtain a degree of differential privacy.Item Bayesian methods for network traffic analysis(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2019., 2019.) Kurt, Barış.; Cemgil, Ali Taylan.Statistical information about tra c patterns help a service provider to characterize its network resource usage and user behavior, infer future tra c demands, detect tra c and usage anomalies, and possibly provide insights to improve the performance of the network. However, the increasingly high volume and speed of data over modern networks make collecting these statistics di cult. Moreover, smarter network attacks require sophisticated detection methods that are able to fuse many network and hardware signals. Fortunately, Bayesian statistical methods are powerful tools that can infer such information under the harsh network environments. In this thesis we apply two Bayesian methods for two speci c network problems. First, we use the Bayesian multiple change models to detect DDoS attacks in SIP networks by fusing the observations coming from the network tra c and the networking hardware. We show that our method is superior to classic DDoS detection methods and using hardware signals improve the detection rate. For this work, we developed a probabilistic SIP network simulator and a monitoring system, and published it as an open-source software. In our second work, we estimated network statistics from a high speed network where we can only observe a fraction of the network tra c. For this problem we develop a generic novel method called ThinNTF, based on non-negative tensor factorization. This method can work with di erent network sampling schemes and recovers original network statistics by detecting the periodic network tra c patterns from the sampled network data and gives better estimates compared to the state of the art.