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1. When Directory Design Meets Data Explosion: Rethinking Query Performance for IoT

   https://doi.org/10.1109/ISNCC49221.2020.9297269  

   Hao, Luoyao ; Schulzrinne, Henning ( October 2020 , 2020 International Symposium on Networks, Computers and Communications (ISNCC))

   null (Ed.)

   As IoT services scale up from single homes to smart cities, directories and mapping services are needed to manage potentially millions of devices. However, directory service providers will likely struggle to accommodate the increasing number of IoT devices, made more challenging by their heterogeneous metadata and the large volume of queries. One of the critical challenges, the high heterogeneity of IoT, is being addressed by a working standard of W3C, which formalizes a physical or virtual device as a formatted Thing Description (TD).We propose a local directory service architecture with a series of design requirements. With a focus on query performance, we build a proof-of-concept system to store metadata of IoT devices as TDs in terms of the working standard. A Raspberry Pi is configured to investigate the query performance of relational database and non-relational database as the classic choices for internal directories. Evaluation results demonstrate that compared with relational database, non-relational database can achieve 2.9 times higher resilience on property query and 2.35 times faster processing on spatial query, with mild loss on aggregation query. 

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2. Demonstration of Chestnut: An In-memory Data Layout Designer for Database Applications

   https://doi.org/10.1145/3318464.3384712  

   Mingwei Samuel, Cong Yan ( June 2020 , ACM SIGMOD Conference)

   null (Ed.)

   This demonstration showcases Chestnut, a data layout generator for in-memory object-oriented database applications. Given an application and a memory budget, Chestnut generates a customized in-memory data layout and the corresponding query plans that are specialized for the application queries. Our demo will let users design and improve simple web applications using Chestnut. Users can view the Chestnut-generated data layouts using a custom visualization system, which will allow users to see how the application parameters affect Chestnut's design. Finally, users will be able to run queries generated by the application via the customized query plans generated by Chestnut or traditional relational query engines, and can compare the results and observe the speedup achieved by the Chestnut-generated query plans. 

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3. Generating Plugs and Data Sockets for Plug-and-Play Database Web Services

   Jain, A. ; Dyreson, C. ; Bhowmick, S.S. ( September 2022 , Cooperative Information Systems - 28th International Conference, CoopIS)

   We propose a novel system for creating data plugs and sockets for plug-and-play database web services. We adopt a plug-and-play approach to couple an application to a database. In our approach a designer constructs a “plug,” which is a simple specification of the output produced by the service. If the plug can be “played” on the database “socket” then the web service is generated. Our plug-and-play approach has three advantages. First, a plug is portable. A plug can be played on any data source to generate a web service. Second, a plug is reliable. The database is checked to ensure that the service can be safely and correctly generated. Third, plug-and-play web services are easier to code for complex data since a service designer can write a simple plug, abstracting away the data’s real complexity. We describe a system for plug-and-play web services and experimentally evaluate the system. 

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4. TransRisk: Mobility Privacy Risk Prediction based on Transferred Knowledge

   https://doi.org/10.1145/3534581  

   Xie, Xiaoyang ; Hong, Zhiqing ; Qin, Zhou ; Fang, Zhihan ; Tian, Yuan ; Zhang, Desheng ( July 2022 , Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Human mobility data may lead to privacy concerns because a resident can be re-identified from these data by malicious attacks even with anonymized user IDs. For an urban service collecting mobility data, an efficient privacy risk assessment is essential for the privacy protection of its users. The existing methods enable efficient privacy risk assessments for service operators to fast adjust the quality of sensing data to lower privacy risk by using prediction models. However, for these prediction models, most of them require massive training data, which has to be collected and stored first. Such a large-scale long-term training data collection contradicts the purpose of privacy risk prediction for new urban services, which is to ensure that the quality of high-risk human mobility data is adjusted to low privacy risk within a short time. To solve this problem, we present a privacy risk prediction model based on transfer learning, i.e., TransRisk, to predict the privacy risk for a new target urban service through (1) small-scale short-term data of its own, and (2) the knowledge learned from data from other existing urban services. We envision the application of TransRisk on the traffic camera surveillance system and evaluate it with real-world mobility datasets already collected in a Chinese city, Shenzhen, including four source datasets, i.e., (i) one call detail record dataset (CDR) with 1.2 million users; (ii) one cellphone connection data dataset (CONN) with 1.2 million users; (iii) a vehicular GPS dataset (Vehicles) with 10 thousand vehicles; (iv) an electronic toll collection transaction dataset (ETC) with 156 thousand users, and a target dataset, i.e., a camera dataset (Camera) with 248 cameras. The results show that our model outperforms the state-of-the-art methods in terms of RMSE and MAE. Our work also provides valuable insights and implications on mobility data privacy risk assessment for both current and future large-scale services. 

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5. Adversarial Attacks for Black-Box Recommender Systems Via Copying Transferable Cross-Domain User Profiles

   https://doi.org/10.1109/TKDE.2023.3272652  

   Fan, Wenqi ; Zhao, Xiangyu ; Li, Qing ; Derr, Tyler ; Ma, Yao ; Liu, Hui ; Wang, Jianping ; Tang, Jiliang ( July 2023 , IEEE Transactions on Knowledge and Data Engineering)

   As widely used in data-driven decision-making, recommender systems have been recognized for their capabilities to provide users with personalized services in many user-oriented online services, such as E-commerce (e.g., Amazon, Taobao, etc.) and Social Media sites (e.g., Facebook and Twitter). Recent works have shown that deep neural networks-based recommender systems are highly vulnerable to adversarial attacks, where adversaries can inject carefully crafted fake user profiles (i.e., a set of items that fake users have interacted with) into a target recommender system to promote or demote a set of target items. Instead of generating users with fake profiles from scratch, in this paper, we introduce a novel strategy to obtain “fake” user profiles via copying cross-domain user profiles, where a reinforcement learning-based black-box attacking framework (CopyAttack+) is developed to effectively and efficiently select cross-domain user profiles from the source domain to attack the target system. Moreover, we propose to train a local surrogate system for mimicking adversarial black-box attacks in the source domain, so as to provide transferable signals with the purpose of enhancing the attacking strategy in the target black-box recommender system. Comprehensive experiments on three real-world datasets are conducted to demonstrate the effectiveness of the proposed attacking framework. 

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