More Like this

1. Are You There?: Identifying Unavailability in Mobile Messaging

   https://doi.org/10.1145/3290607.3312893  

   Jain, Pranut; Farzan, Rosta; Lee, Adam J. (May 2019, Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems)

   Delays in response to mobile messages can cause negative emotions in message senders and can affect an individual's social relationships. Recipients, too, feel a pressure to respond even during inopportune moments. A messaging assistant which could respond with relevant contextual information on behalf of individuals while they are unavailable might reduce the pressure to respond immediately and help put the sender at ease. By modelling attentiveness to messaging, we aim to (1) predict instances when a user is not able to attend to an incoming message within reasonable time and (2) identify what contextual factors can explain the user's attentiveness---or lack thereof---to messaging. In this work, we investigate two approaches to modelling attentiveness: a general approach in which data from a group of users is combined to form a single model for all users; and a personalized approach, in which an individual model is created for each user. Evaluating both models, we observed that on average, with just seven days of training data, the personalized model can outperform the generalized model in terms of both accuracy and F-measure for predicting inattentiveness. Further, we observed that in majority of cases, the messaging patterns identified by the attentiveness models varied widely across users. For example, the top feature in the generalized model appeared in the top five features for only 41% of the individual personalized models. 

   more » « less
2. Personalized Dynamics Models for Adaptive Assistive Navigation Systems

   OhnBar, Eshed and (October 2018, 2nd Conference on Robot Learning)

   Consider an assistive system that guides visually impaired users through speech and haptic feedback to their destination. Existing robotic and ubiquitous navigation technologies (e.g., portable, ground, or wearable systems) often operate in a generic, user-agnostic manner. However, to minimize confusion and navigation errors, our real-world analysis reveals a crucial need to adapt theinstructional guidance across different end-users with diverse mobility skills. To address this practical issue in scalable system design, we propose a novel model based reinforcement learning framework for personalizing the system-user interaction experience. When incrementally adapting the system to new users, we propose to use a weighted experts model for addressing data-efficiency limitations in transfer learning with deep models. A real-world dataset of navigation by blind users is used to show that the proposed approach allows for (1) more accurate long-term human behavior prediction (up to 20 seconds into the future) through improved reasoning over personal mobility characteristics, interaction with surrounding obstacles, and the current navigation goal, and (2) quick adaptation at the onset of learning, when data is limited. 

   more » « less
3. Modeling of Personalized Privacy Disclosure Behavior: A Formal Method Approach

   https://doi.org/10.1145/3465481.3470102  

   Mehdy, A K; Mehrpouyan, Hoda (August 2021, ARES 2021: The 16th International Conference on Availability, Reliability and Security)

   In order to create user-centric and personalized privacy management tools, the underlying models must account for individual users’ privacy expectations, preferences, and their ability to control their information sharing activities. Existing studies of users’ privacy behavior modeling attempt to frame the problem from a request’s perspective, which lack the crucial involvement of the information owner, resulting in limited or no control of policy management. Moreover, very few of them take into the consideration the aspect of correctness, explainability, usability, and acceptance of the methodologies for each user of the system. In this paper, we present a methodology to formally model, validate, and verify personalized privacy disclosure behavior based on the analysis of the user’s situational decision-making process. We use a model checking tool named UPPAAL to represent users’ self-reported privacy disclosure behavior by an extended form of finite state automata (FSA), and perform reachability analysis for the verification of privacy properties through computation tree logic (CTL) formulas. We also describe the practical use cases of the methodology depicting the potential of formal technique towards the design and development of user-centric behavioral modeling. This paper, through extensive amounts of experimental outcomes, contributes several insights to the area of formal methods and user-tailored privacy behavior modeling. 

   more » « less
4. Privacy-Preserving Personalized Federated Learning

   https://doi.org/10.1109/ICC40277.2020.9149207  

   Hu, Rui; Guo, Yuanxiong; Li, Hongning; Pei, Qingqi; Gong, Yanmin (June 2020, ICC 2020 - 2020 IEEE International Conference on Communications (ICC))

   To provide intelligent and personalized services on smart devices, machine learning techniques have been widely used to learn from data, identify patterns, and make automated decisions. Machine learning processes typically require a large amount of representative data that are often collected through crowdsourcing from end users. However, user data could be sensitive in nature, and learning machine learning models on these data may expose sensitive information of users, violating their privacy. Moreover, to meet the increasing demand of personalized services, these learned models should capture their individual characteristics. This paper proposes a privacy-preserving approach for learning effective personalized models on distributed user data while guaranteeing the differential privacy of user data. Practical issues in a distributed learning system such as user heterogeneity are considered in the proposed approach. Moreover, the convergence property and privacy guarantee of the proposed approach are rigorously analyzed. Experiments on realistic mobile sensing data demonstrate that the proposed approach is robust to high user heterogeneity and offer a trade-off between accuracy and privacy. 

   more » « less
5. Personalized Federated Learning with Differential Privacy

   https://doi.org/10.1109/JIOT.2020.2991416  

   Hu, Rui; Guo, Yuanxiong; Li, Hongning; Pei, Qingqi; Gong, Yanmin (April 2020, IEEE Internet of Things Journal)

   To provide intelligent and personalized services on smart devices, machine learning techniques have been widely used to learn from data, identify patterns, and make automated decisions. Machine learning processes typically require a large amount of representative data that are often collected through crowdsourcing from end users. However, user data could be sensitive in nature, and training machine learning models on these data may expose sensitive information of users, violating their privacy. Moreover, to meet the increasing demand of personalized services, these learned models should capture their individual characteristics. This paper proposes a privacy-preserving approach for learning effective personalized models on distributed user data while guaranteeing the differential privacy of user data. Practical issues in a distributed learning system such as user heterogeneity are considered in the proposed approach. In addition, the convergence property and privacy guarantee of the proposed approach are rigorously analyzed. Experimental results on realistic mobile sensing data demonstrate that the proposed approach is robust to user heterogeneity and offers a good trade-off between accuracy and privacy. 

   more » « less