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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Longitudinal attacks against iterative data collection with local differential privacy(Tubitak Scientific & Technological Research Council Turkey, 2024) Department of Computer Engineering; Gürsoy, Mehmet Emre; Department of Computer Engineering; College of EngineeringLocal differential privacy (LDP) has recently emerged as an accepted standard for privacy -preserving collection of users' data from smartphones and IoT devices. In many practical scenarios, users' data needs to be collected repeatedly across multiple iterations. In such cases, although each collection satisfies LDP individually by itself, a longitudinal collection of multiple responses from the same user degrades that user's privacy. To demonstrate this claim, in this paper, we propose longitudinal attacks against iterative data collection with LDP. We formulate a general Bayesian adversary model, and then individually show the application of this adversary model on six popular LDP protocols: GRR, BLH, OLR, RAPPOR, OUE, and SS. We experimentally demonstrate the effectiveness of our attacks using two metrics, three datasets, and various privacy and domain parameters. The effectiveness of our attacks highlights the privacy risks associated with longitudinal data collection in a practical and quantifiable manner and motivates the need for appropriate countermeasures.Publication Metadata only Prototyping products using web-based AI tools: designing a tangible programming environment with children(Association Computing Machinery, 2022) Department of Computer Engineering; Sabuncuoğlu, Alpay; Sezgin, Tevfik Metin; Department of Computer Engineering; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); College of Engineering; Graduate School of Sciences and EngineeringA wide variety of children's products such as mobile apps, toys, and assistant systems now have integrated smart features. Designing such AI-powered products with children, the users, is essential. Using high-fidelity prototypes can be a means to reveal children's needs and behaviors with AI-powered systems. Yet, a prototype that can show unpredictable features similar to the final AI-powered product can be expensive. A more manageable and inexpensive solution is using web-based AI prototyping tools such as Teachable Machine. In this work, we developed a Teachable Machine-powered game-development environment to inform our tangible programming environment's design decisions. Using this kind of an AI-powered high-fidelity prototype in the research process allowed us to observe children in a very similar setting to our final AI-powered product and extract design considerations. This paper reports our experience of prototyping AI-powered solutions with children and shares our design considerations for children's self-made tangible representations.Publication Metadata only Learning Markov Chain Models from sequential data under local differential privacy(Springer Science and Business Media Deutschland Gmbh, 2024) Department of Computer Engineering; Güner, Efehan; Gürsoy, Mehmet Emre; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of EngineeringMarkov chain models are frequently used in the analysis and modeling of sequential data such as location traces, time series, natural language, and speech. However, considering that many data sources are privacy-sensitive, it is imperative to design privacy-preserving methods for learning Markov models. In this paper, we propose Prima for learning discrete-time Markov chain models under local differential privacy (LDP), a state-of-the-art privacy standard. In Prima, each user locally encodes and perturbs their sequential record on their own device using LDP protocols. For this purpose, we adapt two bitvector-based LDP protocols (RAPPOR and OUE); and furthermore, we develop a novel extension of the GRR protocol called AdaGRR. We also propose to utilize custom privacy budget allocation strategies for perturbation, which enable uneven splitting of the privacy budget to better preserve utility in cases with uneven sequence lengths. On the server-side, Prima uses a novel algorithm for estimating Markov probabilities from perturbed data. We experimentally evaluate Prima using three real-world datasets, four utility metrics, and under various combinations of privacy budget and budget allocation strategies. Results show that Prima enables learning Markov chains under LDP with high utility and low error compared to Markov chains learned without privacy constraints.Publication Metadata only Building quadtrees for spatial data under local differential privacy(Springer Science and Business Media Deutschland Gmbh, 2023) Department of Computer Engineering; Alptekin, Ece; Gürsoy, Mehmet Emre; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of EngineeringSpatial decompositions are commonly used in the privacy literature for various purposes such as range query answering, spatial indexing, count-of-counts histograms, data summarization, and visualization. Among spatial decomposition techniques, quadtrees are a popular and well-known method. In this paper, we study the problem of building quadtrees for spatial data under the emerging notion of Local Differential Privacy (LDP). We first propose a baseline solution inspired from a state-of-the-art method from the centralized DP literature and adapt it to LDP. Motivated by the observation that the baseline solution causes large noise accumulation due to its iterative strategy, we then propose a novel solution which utilizes a single data collection step from users, propagates density estimates to all nodes, and finally performs structural corrections to the quadtree. We experimentally evaluate the baseline solution and the proposed solution using four real-world location datasets and three utility metrics. Results show that our proposed solution consistently outperforms the baseline solution, and furthermore, the resulting quadtrees provide high accuracy in practical tasks such as spatial query answering under conventional privacy levels.Publication Metadata only Enhancing resolution and contrast in fibre bundle-based fluorescence microscopy using generative adversarial network(Wiley, 2024) Morova, Berna; Aydin, Musa; Eren, Furkan; Pysz, Dariusz; Buczynski, Ryszard; Department of Physics; Ketabchi, Amir Mohammad; Uysallı, Yiğit; Bavili, Nima; Kiraz, Alper; Department of Physics; Graduate School of Sciences and Engineering; College of SciencesFibre bundle (FB)-based endoscopes are indispensable in biology and medical science due to their minimally invasive nature. However, resolution and contrast for fluorescence imaging are limited due to characteristic features of the FBs, such as low numerical aperture (NA) and individual fibre core sizes. In this study, we improved the resolution and contrast of sample fluorescence images acquired using in-house fabricated high-NA FBs by utilising generative adversarial networks (GANs). In order to train our deep learning model, we built an FB-based multifocal structured illumination microscope (MSIM) based on a digital micromirror device (DMD) which improves the resolution and the contrast substantially compared to basic FB-based fluorescence microscopes. After network training, the GAN model, employing image-to-image translation techniques, effectively transformed wide-field images into high-resolution MSIM images without the need for any additional optical hardware. The results demonstrated that GAN-generated outputs significantly enhanced both contrast and resolution compared to the original wide-field images. These findings highlight the potential of GAN-based models trained using MSIM data to enhance resolution and contrast in wide-field imaging for fibre bundle-based fluorescence microscopy. Lay Description: Fibre bundle (FB) endoscopes are essential in biology and medicine but suffer from limited resolution and contrast for fluorescence imaging. Here we improved these limitations using high-NA FBs and generative adversarial networks (GANs). We trained a GAN model with data from an FB-based multifocal structured illumination microscope (MSIM) to enhance resolution and contrast without additional optical hardware. Results showed significant enhancement in contrast and resolution, showcasing the potential of GAN-based models for fibre bundle-based fluorescence microscopy.Publication Metadata only Effect of preservation period on the viscoelastic material properties of soft tissues with implications for liver transplantation(Asme, 2010) N/A; N/A; N/A; Department of Mechanical Engineering; Department of Mechanical Engineering; Öcal, Sina; Özcan, Mustafa Umut; Başdoğan, İpek; Başdoğan, Çağatay; Master Student; Master Student; Faculty Member; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; 179940; 125489The liver harvested from a donor must be preserved and transported to a suitable recipient immediately for a successful liver transplantation. In this process, the preservation period is the most critical, since it is the longest and most tissue damage occurs during this period due to the reduced blood supply to the harvested liver and the change in its temperature. We investigate the effect of preservation period on the dynamic material properties of bovine liver using a viscoelastic model derived from both impact and ramp and hold experiments. First, we measure the storage and loss moduli of bovine liver as a function of excitation frequency using an impact hammer. Second, its time-dependent relaxation modulus is measured separately through ramp and hold experiments performed by a compression device. Third, a Maxwell solid model that successfully imitates the frequency- and time-dependent dynamic responses of bovine liver is developed to estimate the optimum viscoelastic material coefficients by minimizing the error between the experimental data and the corresponding values generated by the model. Finally, the variation in the viscoelastic material coefficients of bovine liver are investigated as a function of preservation period for the liver samples tested 1 h, 2 h, 4 h, 8 h, 12 h, 24 h, 36 h, and 48 h after harvesting. The results of our experiments performed with three animals show that the liver tissue becomes stiffer and more viscous as it spends more time in the preservation cycle.Publication Metadata only Decomposition of lambda K-nu into kites and 4-cycles(Charles Babbage Research Centre, 2017) Milici, Salvatore; Department of Mathematics; Küçükçifçi, Selda; Faculty Member; Department of Mathematics; College of Sciences; 105252Given a collection of graphs H, an H-decomposition of λkv is a decomposition of the edges of λKv into isomorphic copies of graphs in Ti. A kite is a triangle with a tail consisting of a single edge. In this paper we investigate the decomposition problem when H is the set containing a kite and a 4-cycle, that is; this paper gives a complete solution to the problem of decomposing λKv into r kites and s 4-cycles for every admissible values of v, λ, r and s.Publication Metadata only The distribution of the relative arc density of a family of interval catch digraph based on uniform data(Springer, 2012) Department of Mathematics; Ceyhan, Elvan; Faculty Member; Department of Mathematics; College of Sciences; N/AWe study a family of interval catch digraph called proportional-edge proximity catch digraph (PCD) which is also a special type of intersection digraphs parameterized with an expansion and a centrality parameter. PCDs are random catch digraphs that have been developed recently and have applications in classification and spatial pattern analysis. We investigate a graph invariant of the PCDs called relative arc density. We demonstrate that relative arc density of PCDs is a U-statistic and using the central limit theory of U-statistics, we derive the (asymptotic) distribution of the relative arc density of proportional-edge PCD for uniform data in one dimension. We also determine the parameters for which the rate of convergence to asymptotic normality is fastest.Publication Metadata only Spatial clustering tests based on the domination number of a new random digraph family(Taylor & Francis Inc, 2011) Department of Mathematics; Ceyhan, Elvan; Faculty Member; Department of Mathematics; College of Sciences; N/AWe use the domination number of a parametrized random digraph family called proportional-edge proximity catch digraphs (PCDs) for testing multivariate spatial point patterns. This digraph family is based on relative positions of data points from various classes. We extend the results on the distribution of the domination number of proportional-edge PCDs, and use the domination number as a statistic for testing segregation and association against complete spatial randomness. We demonstrate that the domination number of the PCD has binomial distribution when size of one class is fixed while the size of the other (whose points constitute the vertices of the digraph) tends to infinity and has asymptotic normality when sizes of both classes tend to infinity. We evaluate the finite sample performance of the test by Monte Carlo simulations and prove the consistency of the test under the alternatives. We find the optimal parameters for testing each of the segregation and association alternatives. Furthermore, the methodology discussed in this article is valid for data in higher dimensions also.Publication Metadata only Cell-specific and post-hoc spatial clustering tests based on nearest neighbor contingency tables(Korean Statistical Soc, 2017) Department of Mathematics; Ceyhan, Elvan; Faculty Member; Department of Mathematics; College of Sciences; N/ASpatial clustering patterns in a multi-class setting such as segregation and association between classes have important implications in various fields, e.g., in ecology, and can be tested using nearest neighbor contingency tables (NNCTs). a NNCT is constructed based on the types of the nearest neighbor (NN) pairs and their frequencies. We survey the cell-specific (or pairwise) and overall segregation tests based on NNCTs in literature and introduce new ones and determine their asymptotic distributions. We demonstrate that cell-specific tests enjoy asymptotic normality, while overall tests have chi-square distributions asymptotically. Some of the overall tests are confounded by the unstable generalized inverse of the rank-deficient covariance matrix. To overcome this problem, we propose rank-based corrections for the overall tests to stabilize their behavior. We also perform an extensive' Monte Carlo simulation study to compare the finite sample performance of the tests in terms of empirical size and power based on the asymptotic and Monte Carlo critical values and determine the tests that have the best size and power performance and are robust to differences in relative abundances (of the classes). in addition to the cell-specific tests, we discuss one(-class)-versus-rest type of tests as post-hoc,tests after a significant overall test. We also introduce the concepts of total, strong, and partial segregatioN/Association to differentiate different levels of these patterns. We compare the new tests with the existing NNCT-tests in literature with simulations and illustrate the tests on an ecological data set. (C) 2016 the Korean Statistical Society. Published by Elsevier B.V. all rights reserved.