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Open Access Publications from the University of California

Scholarly Works (5 results)

  • Thesis
  • Peer Reviewed
UC Irvine Electronic Theses and Dissertations (2022)

Unprecedented growth in sensing, data capture devices, communication, and computing technologies have enabled us to capture and analyze almost every aspect of our lives. Such a prospect has resulted in the development of a myriad of applications in the cyber-physical systems, and the Internet of Things domain. While limitless possibilities exist, their realization has led to the grand challenge of designing a new class of systems that can provideseamless support for efficient and easier to program sensor-based applications. Furthermore, as sensor data processing often consists of complex compiled code, expensive machine learning, and signal processing code, systems that can scale to data generated by a myriad of interconnected sensors and devices embedded in the environment are needed.

This thesis presents TippersDB, a middleware system designed to build sensor-based smart space analytical applications. The intended goal of TippersDB is to ease the task of developing complex smart space applications, to develop an extensible system that allows diverse/heterogeneous sensors (with possibly overlapping functionalities) to co-exist and to be seamlessly integrated into the system without requiring application redesign, and to exploit the semantics of the physical world, as well as, sensor capabilities to optimize the applications. TippersDB provides a powerful data model that decouples semantic data about the application domain from sensor data using which the semantic data is derived. By supporting mechanisms to translate data, concepts, and queries between the two levels, TippersDB relieves the application developers from having to know or reason about either the type or location of sensors or write sensor specific code. In addition, it allows for multiple optimizations based on smart space semantics to improve query processing.

In particular, in this thesis, first we introduce the SmartBench benchmark that we developed to analyze the existing database technologies in terms of their strength/weaknesses and suitability in supporting IoT applications. Next, we introduce the TippersDB data model and describe its query-driven translation of sensor data. We then provide a summary of the system implementation and show a performance evaluation of TippersDB using IoT benchmark queries. This is followed by the description of the progressive query processing techniques used in TippersDB to reduce wait time for the users by providing them with early results. Finally, we highlight the benefits of TippersDB through a case study.

    Cover page: A Semantic Approach To Data Management for Smart Spaces
    • Article
    • Peer Reviewed
    UC Irvine Previously Published Works (2021)
    This paper proposes a system, entitled Concealer that allows sharing time-varying spatial data (e.g., as produced by sensors) in encrypted form to an untrusted third-party service provider to provide location-based applications (involving aggregation queries over selected regions over time windows) to users. Concealer exploits carefully selected encryption techniques to use indexes supported by database systems and combines ways to add fake tuples in order to realize an efficient system that protects against leakage based on output-size. Thus, the design of Concealer overcomes two limitations of existing symmetric searchable encryption (SSE) techniques: (i) it avoids the need of specialized data structures that limit usability/practicality of SSE in large scale deployments, and (ii) it avoids information leakages based on the output-size, which may leak data distributions. Experimental results validate the efficiency of the proposed algorithms over a spatial time-series dataset (collected from a smart space) and TPC-H datasets, each of 136 Million rows, the size of which prior approaches have not scaled to.
      Cover page: Concealer: SGX-based Secure, Volume Hiding, and Verifiable Processing of Spatial Time-Series DatasetsCreative Commons 'BY' version 4.0 license
      • Article
      • Peer Reviewed
      UC Irvine Previously Published Works (2021)
      Data outsourcing allows data owners to keep their data at \emph{untrusted} clouds that do not ensure the privacy of data and/or computations. One useful framework for fault-tolerant data processing in a distributed fashion is MapReduce, which was developed for \emph{trusted} private clouds. This paper presents algorithms for data outsourcing based on Shamir's secret-sharing scheme and for executing privacy-preserving SQL queries such as count, selection including range selection, projection, and join while using MapReduce as an underlying programming model. Our proposed algorithms prevent an adversary from knowing the database or the query while also preventing output-size and access-pattern attacks. Interestingly, our algorithms do not involve the database owner, which only creates and distributes secret-shares once, in answering any query, and hence, the database owner also cannot learn the query. Logically and experimentally, we evaluate the efficiency of the algorithms on the following parameters: (\textit{i}) the number of communication rounds (between a user and a server), (\textit{ii}) the total amount of bit flow (between a user and a server), and (\textit{iii}) the computational load at the user and the server.\B
        Cover page: Privacy-Preserving Secret Shared Computations Using MapReduceCreative Commons 'BY' version 4.0 license
        • Article
        • Peer Reviewed
        UC Irvine Previously Published Works (2020)
        The growing deployment of Internet of Things (IoT) systems aims to ease the daily life of end-users by providing several value-added services. However, IoT systems may capture and store sensitive, personal data about individuals in the cloud, thereby jeopardizing user-privacy. Emerging legislation, such as California's CalOPPA and GDPR in Europe, support strong privacy laws to protect an individual's data in the cloud. One such law relates to strict enforcement of data retention policies. This paper proposes a framework, entitled IoT Expunge that allows sensor data providers to store the data in cloud platforms that will ensure enforcement of retention policies. Additionally, the cloud provider produces verifiable proofs of its adherence to the retention policies. Experimental results on a real-world smart building testbed show that IoT Expunge imposes minimal overheads to the user to verify the data against data retention policies.
          Cover page: IoT ExpungeCreative Commons 'BY' version 4.0 license
          • Article
          • Peer Reviewed
          UC Irvine Previously Published Works (2020)
          Contact tracing has emerged as one of the main mitigation strategies to prevent the spread of pandemics such as COVID-19. Recently, several efforts have been initiated to track individuals, their movements, and interactions using technologies, e.g., Bluetooth beacons, cellular data records, and smartphone applications. Such solutions are often intrusive, potentially violating individual privacy rights and are often subject to regulations (e.g., GDPR and CCPR) that mandate the need for opt-in policies to gather and use personal information. In this paper, we introduce Quest, a system that empowers organizations to observe individuals and spaces to implement policies for social distancing and contact tracing using WiFi connectivity data in a passive and privacy-preserving manner. The goal is to ensure the safety of employees and occupants at an organization, while protecting the privacy of all parties. Quest incorporates computationally- and information-theoretically-secure protocols that prevent adversaries from gaining knowledge of an individual's location history (based on WiFi data); it includes support for accurately identifying users who were in the vicinity of a confirmed patient, and then informing them via opt-in mechanisms. Quest supports a range of privacy-enabled applications to ensure adherence to social distancing, monitor the flow of people through spaces, identify potentially impacted regions, and raise exposure alerts. We describe the architecture, design choices, and implementation of the proposed security/privacy techniques in Quest. We, also, validate the practicality of Quest and evaluate it thoroughly via an actual campus-scale deployment at UC Irvine over a very large dataset of over 50M tuples.
            Cover page: Quest: Practical and Oblivious Mitigation Strategies for COVID-19 using WiFi DatasetsCreative Commons 'BY' version 4.0 license
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