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1. Formal specification and verification of user-centric privacy policies for ubiquitous systems

   https://doi.org/10.1145/3331076.3331105  

   Joshaghani, Rezvan ; Black, Stacy ; Sherman, Elena ; Mehrpouyan, Hoda ( January 2019 , the 23rd International Database Applications & Engineering Symposium)

   null (Ed.)

   As our society has become more information oriented, each individual is expressed, defined, and impacted by information and information technology. While valuable, the current state-of-the-art mostly are designed to protect the enterprise/ organizational privacy requirements and leave the main actor, i.e., the user, un-involved or with the limited ability to have control over his/her information sharing practices. In order to overcome these limitations, algorithms and tools that provide a user-centric privacy management system to individuals with different privacy concerns are required to take into the consideration the dynamic nature of privacy policies which are constantly changing based on the information sharing context and environmental variables. This paper extends the concept of contextual integrity to provide mathematical models and algorithms that enables the creations and management of privacy norms for individual users. The extension includes the augmentation of environmental variables, i.e. time, date, etc. as part of the privacy norms, while introducing an abstraction and a partial relation over information attributes. Further, a formal verification technique is proposed to ensure privacy norms are enforced for each information sharing action. 

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2. A User-Centric and Sentiment Aware Privacy-Disclosure Detection Framework based on Multi-input Neural Network

   Mehdy, A K ; Mehrpouyan, Hoda ( January 2020 , the PrivateNLP 2020 Workshop on Privacy and Natural Language Processing Colocated with 13th ACM International WSDM Conference, 2020, in Houston, Texas, USA.)

   null (Ed.)

   Data and information privacy is a major concern of today’s world. More specifically, users’ digital privacy has become one of the most important issues to deal with, as advancements are being made in information sharing technology. An increasing number of users are sharing information through text messages, emails, and social media without proper awareness of privacy threats and their consequences. One approach to prevent the disclosure of private information is to identify them in a conversation and warn the dispatcher before the conveyance happens between the sender and the receiver. Another way of preventing information (sensitive) loss might be to analyze and sanitize a batch of offline documents when the data is already accumulated somewhere. However, automating the process of identifying user-centric privacy disclosure in textual data is challenging. This is because the natural language has an extremely rich form and structure with different levels of ambiguities. Therefore, we inquire after a potential framework that could bring this challenge within reach by precisely recognizing users’ privacy disclosures in a piece of text by taking into account - the authorship and sentiment (tone) of the content alongside the linguistic features and techniques. The proposed framework is considered as the supporting plugin to help text classification systems more accurately identify text that might disclose the author’s personal or private information. 

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3. A Model-Checking Approach for Enforcing Purpose-Based Privacy Policies

   https://doi.org/10.1109/PAC.2017.31  

   Joshaghani, Rezvan ; Mehrpouyan, Hoda ( December 2017 , 2017 IEEE Symposium on Privacy-Aware Computing (PAC))

   null (Ed.)

   With the growth of Internet in many different aspects of life, users are required to share private information more than ever. Hence, users need a privacy management tool that can enforce complex and customized privacy policies. In this paper, we propose a privacy management system that not only allows users to define complex privacy policies for data sharing actions, but also monitors users' behavior and relationships to generate realistic policies. In addition, the proposed system utilizes formal modeling and model-checking approach to prove that information disclosures are valid and privacy policies are consistent with one another 

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4. Visualizing Privacy-Utility Trade-Offs in Differentially Private Data Releases

   https://doi.org/10.2478/popets-2022-0058  

   Nanayakkara, Priyanka ; Bater, Johes ; He, Xi ; Hullman, Jessica ; Rogers, Jennie ( March 2022 , Proceedings on Privacy Enhancing Technologies)

   Abstract Organizations often collect private data and release aggregate statistics for the public’s benefit. If no steps toward preserving privacy are taken, adversaries may use released statistics to deduce unauthorized information about the individuals described in the private dataset. Differentially private algorithms address this challenge by slightly perturbing underlying statistics with noise, thereby mathematically limiting the amount of information that may be deduced from each data release. Properly calibrating these algorithms—and in turn the disclosure risk for people described in the dataset—requires a data curator to choose a value for a privacy budget parameter, ɛ . However, there is little formal guidance for choosing ɛ , a task that requires reasoning about the probabilistic privacy–utility tradeoff. Furthermore, choosing ɛ in the context of statistical inference requires reasoning about accuracy trade-offs in the presence of both measurement error and differential privacy (DP) noise. We present Vi sualizing P rivacy (ViP), an interactive interface that visualizes relationships between ɛ , accuracy, and disclosure risk to support setting and splitting ɛ among queries. As a user adjusts ɛ , ViP dynamically updates visualizations depicting expected accuracy and risk. ViP also has an inference setting, allowing a user to reason about the impact of DP noise on statistical inferences. Finally, we present results of a study where 16 research practitioners with little to no DP background completed a set of tasks related to setting ɛ using both ViP and a control. We find that ViP helps participants more correctly answer questions related to judging the probability of where a DP-noised release is likely to fall and comparing between DP-noised and non-private confidence intervals. 

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5. Privacy as a Planned Behavior: Effects of Situational Factors on Privacy Perceptions and Plans

   https://doi.org/10.1145/3450613.3456829  

   Mehdy, A K ; Ekstrand, Michael D. ; Knijnenburg, Bart P. ; Mehrpouyan, Hoda ( June 2021 , UMAP ’21, June 21–25, 2021, Utrecht, Netherlands © 2021 Association for Computing Machinery.)

   null (Ed.)

   To account for privacy perceptions and preferences in user models and develop personalized privacy systems, we need to understand how users make privacy decisions in various contexts. Existing studies of privacy perceptions and behavior focus on overall tendencies toward privacy, but few have examined the context-specific factors in privacy decision making. We conducted a survey on Mechanical Turk (N=401) based on the theory of planned behavior (TPB) to measure the way users’ perceptions of privacy factors and intent to disclose information are affected by three situational factors embodied hypothetical scenarios: information type, recipients’ role, and trust source. Results showed a positive relationship between subjective norms and perceived behavioral control, and between each of these and situational privacy attitude; all three constructs are significantly positively associated with intent to disclose. These findings also suggest that, situational factors predict participants’ privacy decisions through their influence on the TPB constructs. 

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