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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3

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    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 Engineering
    Markov 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.
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    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 Engineering
    Spatial 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.
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    Attenuation-based hybrid RF/FSO link using soft switching
    (2021) Minhas, Abid Ali; Khan, Muhammad Saeed; Henna, Shagufta; N/A; Iqbal, Muhammad Shahid; Researcher; Graduate School of Sciences and Engineering; N/A
    Due to high data rates, license-free spectrum, and immunity to electromagnetic interference, free-space optical (FSO) links are being considered as a potential candidate to meet the ever-increasing traffic demands of users. The FSO links remain less explored, as their performance depends on environmental conditions such as dust, fog, and clouds. Such conditions do not affect the radio frequency (RF) links in a similar way; however, RF resources are scarce. As an ultimate solution to this performance/scarcity dilemma, we propose a fuzzy logic-based hybrid architecture of FSO and RF links, which can be used to enhance reliability and resource efficiency. We present an intelligent soft switching mechanism between FSO and RF links using a fuzzy inference system to achieve the maximum link reliability and provide heterogeneous wireless services.
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    Energy efficient utilization of IEEE 802.11 hot spots in 3G wireless packet data networks
    (Springer-Verlag Berlin, 2006) N/A; N/A; Akgül, Ferit Ozan; Sunay, Mehmet Oğuz; Master Student; Faculty Member; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A
    The third generation wireless networks and wireless local networks possess complementary characteristics. Recently, there has been significant interest in providing algorithms and specifications that enable their inter-operability. In this paper we propose a novel cross-network, cross-layer algorithm that jointly performs 3G resource allocation and adhoc mode WLAN routing towards effectively increasing the performance of the 3G system. The metrics used in this joint design ensures that multi-user diversity is exploited without causing user starvation in the 3G system and the WLAN assistance does not cause an unfair treatment to any of the mobiles from a battery usage point of view. Furthermore, the design attempts to select the WLAN route so that the assistance does not become a major part of the internal WLAN traffic.