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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 Large language models as a rapid and objective tool for pathology report data extraction(Federation Turkish Pathology Soc., 2024) Department of Computer Engineering; Bolat, Beyza; Eren, Özgür Can; Dur Karasayar, Ayşe Hümeyra; Meriçöz, Çisel Aydın; Demir, Çiğdem Gündüz; Kulaç, İbrahim; Department of Computer Engineering; Koç Üniversitesi İş Bankası Enfeksiyon Hastalıkları Uygulama ve Araştırma Merkezi (EHAM) / Koç University İşbank Center for Infectious Diseases (KU-IS CID); Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); School of Medicine; Graduate School of Health Sciences; College of EngineeringMedical institutions continuously create a substantial amount of data that is used for scientific research. One of the departments with a great amount of archived data is the pathology department. Pathology archives hold the potential to create a case series of valuable rare entities or large cohorts of common entities. The major problem in creation of these databases is data extraction which is still commonly done manually and is highly laborious and error prone. For these reasons, we offer using large language models to overcome these challenges. Ten pathology reports of selected resection specimens were retrieved from electronic archives of Ko & ccedil; University Hospital for the initial set. These reports were de-identified and uploaded to ChatGPT and Google Bard. Both algorithms were asked to turn the reports in a synoptic report format that is easy to export to a data editor such as Microsoft Excel or Google Sheets. Both programs created tables with Google Bard facilitating the creation of a spreadsheet from the data automatically. In conclusion, we propose the use of AI-assisted data extraction for academic research purposes, as it may enhance efficiency and precision compared to manual data entry.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 Could DTI unlock the mystery of subjective tinnitus: it’s time for parameters that go a little out of the routine(Springer, 2024) Yilmaz, Eren; Yildirim, Duzgun; Sanli, Deniz Esin Tekcan; Elpen, Pinar; Tuzuner, Filiz Gosterisli; Sirin, Ahmet; Yagimli, Mustafa; Tozan, Hakan; Sanli, Ahmet Necati; Kandemirli, Sedat Giray; Department of Computer Engineering; İnan, Neslihan Gökmen; Department of Computer Engineering; College of EngineeringIn this study, it was aimed to assess the microstructural changes in the main central auditory pathway in cases with subjective tinnitus. In total, 101 subjects (52 cases with bilateral subjective non-pulsatile tinnitus and 49 healthy cases as the control group) were included in the study. Participants underwent pure tone audiogram and Diffusion Tensor Imaging-Magnetic Resonance Imaging (DTI-MRI) examination with a 3 Tesla MRI device. The number of tracts, tract length, volume, and quantitative anisotropy (QA) and normalized quantitative anisotropy’ (nQA) values were calculated by plotting cochleocortical pathways from the cochlear nerve to ipsilateral and contralateral Heschl’s gyrus (HG). In pure tone audiometry, the control group had lower hearing thresholds than cases with tinnitus. Fibres and nQA values from the right cochlear nerve to the right HG were significantly lower in the tinnitus group than in the control group. Cochlear nuclei voxel counts were significantly decreased in the tinnitus group. Both cochlear nucleus volumes were higher in the tinnitus group than in the control group. nQA values in both cochlear nuclei were decreased in the tinnitus group. This study showed that the most commonly affected part in subjective non-pulsatile tinnitus cases is the cochlear nucleus. Therefore, the cochlear nucleus should be evaluated more carefully in cases presenting with subjective tinnitus.Publication Metadata only Nonintrusive AMR asynchrony for communication optimization(Springer International Publishing Ag, 2017) Nguyen, Tan; Zhang, Weiqun; Almgren, Ann; Shalf, John; N/A; Department of Computer Engineering; Farooqi, Muhammad Nufail; Erten, Didem Unat; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 219274Adaptive Mesh Refinement (AMR) is a well known method for efficiently solving partial differential equations. A straightforward AMR algorithm typically exhibits many synchronization points even during a single time step, where costly communication often degrades the performance. This problem will be even more pronounced on future supercomputers containing billion way parallelism, which will raise the communication cost further. Re-designing AMR algorithms to avoid synchronization is not a viable solution due to the large code size and complex control structures. We present a nonintrusive asynchronous approach to hiding the effects of communication in an AMR application. Specifically, our approach reasons about data dependencies automatically using domain knowledge about AMR applications, allowing asynchrony to be discovered with only a modest amount of code modification. Using this approach, we optimize the synchronous AMR algorithm in the BoxLib software framework without severely affecting the productivity of the application programmer We observe around 27-31% performance improvement for an advection solver on the Hazel Hen supercomputer using 12288 cores.Publication Metadata only Run-time verification of optimistic concurrency(Springer, 2010) Qadeer, Shaz; N/A; Department of Computer Engineering; Department of Computer Engineering; Sezgin, Ali; Taşıran, Serdar; Muşlu, Kıvanç; Researcher; Faculty Member; Undergraduate Student; Department of Computer Engineering; N/A; College of Engineering; College of Engineering; N/A; N/A; N/AAssertion based specifications are not suitable for optimistic concurrency where concurrent operations are performed assuming no conflict among threads and correctness is cast in terms of the absence or presence of conflicts that happen in the future. What is needed is a formalism that allows expressing constraints about the future. In previous work, we introduced tressa claims and incorporated prophecy variables as one such formalism. We investigated static verification of tressa claims and how tressa claims improve reduction proofs. In this paper, we consider tressa claims in the run-time verification of optimistic concurrency implementations. We formalize, via a simple grammar, the annotation of a program with tressa claims. Our method relieves the user from dealing with explicit manipulation of prophecy variables. We demonstrate the use of tressa claims in expressing complex properties with simple syntax. We develop a run-time verification framework which enables the user to evaluate the correctness of tressa claims. To this end, we first describe the algorithms for monitor synthesis which can be used to evaluate the satisfaction of a tressa claim over a given execution. We then describe our tool implementing these algorithms. We report our initial test results.Publication Metadata only A second-order adaptive network model for organizational learning and usage of mental models for a team of match officials(2022) Kuilboer, Sam; Sieraad, Wesley; van Ments, Laila; Treur, Jan; Department of Computer Engineering; Canbaloğlu, Gülay; Undergraduate Student; Department of Computer Engineering; College of Engineering; N/AThis paper describes a multi-level adaptive network model for mental processes making use of shared mental models in the context of organizational learning in team-related performances. The paper describes the value of using shared mental models to illustrate the concept of organizational learning, and factors that influence team performances by using the analogy of a team of match officials during a game of football and show their behavior in a simulation of the shared mental model. The paper discusses potential elaborations of the different studied concepts, as well as implications of the paper in the domain of teamwork and team performance, and in terms of organizational learning.Publication Metadata only Structured adaptive mesh refinement adaptations to retain performance portability with increasing heterogeneity(IEEE Computer Society, 2021) Dubey, Anshu; Berzins, Martin; Burstedde, Carsten; Norman, Michael L.; Wahib, Mohammed; Department of Computer Engineering; Erten, Didem Unat; Faculty Member; Department of Computer Engineering; College of Engineering; 219274Adaptive mesh refinement (AMR) is an important method that enables many mesh-based applications to run at effectively higher resolution within limited computing resources by allowing high resolution only where really needed. This advantage comes at a cost, however: greater complexity in the mesh management machinery and challenges with load distribution. With the current trend of increasing heterogeneity in hardware architecture, AMR presents an orthogonal axis of complexity. The usual techniques, such as asynchronous communication and hierarchy management for parallelism and memory that are necessary to obtain reasonable performance are very challenging to reason about with AMR. Different groups working with AMR are bringing different approaches to this challenge. Here, we examine the design choices of several AMR codes and also the degree to which demands placed on them by their users influence these choices.