Remote Patient Monitoring (RPM) devices transmit patients’ medical indicators (e.g., blood pressure) from the patient's home to their healthcare in order to monitor chronic conditions such as hypertension.

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We investigated how the severity of users’ medical condition (normal vs. high blood pressure), security risk (low vs. modest vs. high risk), and medical advice source (human doctor vs. AI) influence user perceptions of advisor trustworthiness and willingness to disclose RPM-acquired information.

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We found that, on average, participants’ willingness to disclose RPM-acquired health information and trust in the advice source were higher when they believed the advice would come from the doctor than from the AI. Interestingly, we also observed that at any level of trust in the advice source, rather than demonstrating algorithmic aversion, people display evidence of algorithmic appreciation, with respect to a greater willingness to disclose health information to the AI than a doctor.

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See our Medium post here.

Check out our published paper.

We evaluated a support application to help older adults to address mobile phishing attacks, whether the help should receive from social (family and friends) or a volunteer.

 

The support application allows seekers to take a screenshot of a mobile situation, draw and write on the screenshot, and send the helpers. Then, the helpers can respond to the seeker and write explanations on how to address the situation.

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See the demo here.

Read more

Predicting supportable moments to support mobile security and privacy situation is a challenging problem. When help is not necessary, it may interrupt the user, and without help, the user is left struggling with the problem alone. We used models (linear mixed-effects, random forest, XGBoost) to predict the willingness to receive support with measures of mobile situation, user characteristics, and user interaction.

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The random forest was the best model with an accuracy of 78%, a macro F1 of 73%, and a weighted F1 of 76% (with a threshold of 3.25).

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Publish at IMWUT March 2022.

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Investigating how older adults (people aged 65 or more) utilize social support from family and friends when facing security and privacy challenges on their mobile phones. We interviewed 18 older adults about their existing support experiences and used the think-aloud method to gather data about a prototype for providing social support during mobile safety challenges.

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Our findings point to the potential of social support technologies
to aid older adults in mobile safety. We show that social support can bridge technology language barriers and has the potential to help older adults become less dependent on others. It can also stimulate intergenerational conversations that allow older adults to discuss and question existing technological norms that they deem problematic.

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We also evaluate a prototype for social support technologies that
allow older adults to engage in support interaction with their
social connections.

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Publish at mobileHCI2021.

Investigate the experience of the people providing help to older adults.

We quantitatively estimate the factors that affect helpers’ willingness to offer assistance to older relatives regarding mobile security and privacy problems.

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Our results show that family members were more willing to help older adults more with security and privacy issues than other social groups and could guide them due to their familiarity with the older adults’ preferences.

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Publish at IMWUT December 2019.

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Presented at UBICOMP/ISWC 2020. Watch the presentation here.

I analyze the challenges and barriers to providing and receiving online help to older adults in mobile environments and suggest ways to overcome them.

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Our finding suggested that adult children are more than willing to assist older relatives with their technology needs, but the frequency in which this occurs is rare.

 

You can find more details about my Ph.D. in Doctoral Colloquium at UBICOMP/ISWC 2019.

This project was part of my master's degree. I examined how different sources (strong-tie social relations, weak-tie social relations, and authoritative entities) influence the perceived behavioral intention to adopt privacy behavior. We conducted a randomized experiment using a custom Facebook application that collects feedback from participants regarding their intention to adopt privacy practices.

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Our findings have shown that the source of social influence affects the susceptibility to adopt certain privacy behaviors and that there are
different patterns of influence for security and privacy norms

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Publish at CSCW 2017.

This project also was part of my master's degree. We designed and built a technological prototype called “Peteks” – an application based on the Google Chrome browser extension platform.

 

Peteks (notes, in Hebrew) allows the user to create notes on any webpage and share this knowledge with others. We implemented two main view options in Peteks: "public" and "Facebook friends".

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Peteks allows the user to carry out some action on any website, adding a note, reading a note, and even "like" a note. The application can connect to Facebook and derive the user's friends who use the Peteks application.