8 citations · 28 across the 14 of their papers we have counts for
17 papers
Move Fast and Mend Things: Keeping Up with Evolving AI Harms Using Social Media Commentary
Jacqueline Rowe, Animesh Srivastava, Sai Teja Peddinti +2
The rapid deployment of AI systems has created socio-technical, psychological, and operational harms that can elude ex-ante threat modelling and ex-post incident tracking. We intro…
HelpBench: Assessing the Ability of LLMs to Provide Privacy, Safety, and Security Advice
Sarah Meiklejohn, Sunny Consolvo, Patrick Gage Kelley +5
This paper introduces HelpBench, a benchmark for assessing whether LLMs are capable of providing accurate help in response to questions about digital privacy, safety, and security.…
Understanding U.S. Users' Security and Privacy Transparency Needs for Consumer-Facing Generative AI
Jiaxun Cao, Yu Dong, Chunxi Zhan +3
Users increasingly rely on consumer-facing generative AI (GenAI) for tasks ranging from everyday needs to sensitive use cases. Yet, it remains unclear whether and how existing secu…
Understanding Help Seeking for Digital Privacy, Safety, and Security
Kurt Thomas, Sai Teja Peddinti, Sarah Meiklejohn +7
The complexity of navigating digital privacy, safety, and security threats often falls directly on users. This leads to users seeking help from family and peers, platforms and advi…
Automated Generation of Accurate Privacy Captions From Android Source Code Using Large Language Models
Vijayanta Jain, Sepideh Ghanavati, Sai Teja Peddinti +1
Privacy captions are short sentences that succinctly describe what personal information is used, how it is used, and why, within an app. These captions can be utilized in various n…
LLM-Powered Analysis of IoT User Reviews: Tracking and Ranking Security and Privacy Concerns
Taufiq Islam Protick, Sai Teja Peddinti, Nina Taft +1
Being able to understand the security and privacy (S&P) concerns of IoT users brings benefits to both developers and users. To learn about users' views, we examine Amazon IoT revie…
What's on My Network? Using Large Language Models to Identify Real-World IoT Devices at Scale
Rameen Mahmood, Tousif Ahmed, Sai Teja Peddinti +1
The growth of IoT devices in shared environments has outpaced our ability to identify them, posing urgent risks to privacy, safety, and accountability. This challenge is especially…
"We are not Future-ready": Understanding AI Privacy Risks and Existing Mitigation Strategies from the Perspective of AI Developers in Europe
Alexandra Klymenko, Stephen Meisenbacher, Patrick Gage Kelley +3
The proliferation of AI has sparked privacy concerns related to training data, model interfaces, downstream applications, and more. We interviewed 25 AI developers based in Europe…
Beyond PII: How Users Attempt to Estimate and Mitigate Implicit LLM Inference
Synthia Wang, Sai Teja Peddinti, Nina Taft +1
Large Language Models (LLMs) such as ChatGPT can infer personal attributes from seemingly innocuous text, raising privacy risks beyond memorized data leakage. While prior work has…
Which Code Statements Implement Privacy Behaviors in Android Applications?
Chia-Yi Su, Aakash Bansal, Vijayanta Jain +3
A "privacy behavior" in software is an action where the software uses personal information for a service or a feature, such as a website using location to provide content relevant…
Privacy Risks of General-Purpose AI Systems: A Foundation for Investigating Practitioner Perspectives
Stephen Meisenbacher, Alexandra Klymenko, Patrick Gage Kelley +3
The rise of powerful AI models, more formally (GPAIS), has led to impressive leaps in performance across a wide range of tasks. At the same ti…
LLMs for Cyber Security: New Opportunities
Dinil Mon Divakaran, Sai Teja Peddinti
Large language models (LLMs) are a class of powerful and versatile models that are beneficial to many industries. With the emergence of LLMs, we take a fresh look at cyber security…
Evaluating Privacy Perceptions, Experience, and Behavior of Software Development Teams
Maxwell Prybylo, Sara Haghighi, Sai Teja Peddinti +1
With the increase in the number of privacy regulations, small development teams are forced to make privacy decisions on their own. In this paper, we conduct a mixed-method survey s…
A Decade of Privacy-Relevant Android App Reviews: Large Scale Trends
Omer Akgul, Sai Teja Peddinti, Nina Taft +4
We present an analysis of 12 million instances of privacy-relevant reviews publicly visible on the Google Play Store that span a 10 year period. By leveraging state of the art NLP…
Towards Fine-Grained Localization of Privacy Behaviors
Vijayanta Jain, Sepideh Ghanavati, Sai Teja Peddinti +1
Mobile applications are required to give privacy notices to users when they collect or share personal information. Creating consistent and concise privacy notices can be a challeng…
PriGen: Towards Automated Translation of Android Applications' Code to Privacy Captions
Vijayanta Jain, Sanonda Datta Gupta, Sepideh Ghanavati +1
Mobile applications are required to give privacy notices to the users when they collect or share personal information. Creating consistent and concise privacy notices can be a chal…
Mining Anonymity: Identifying Sensitive Accounts on Twitter
Sai Teja Peddinti, Keith W. Ross, Justin Cappos
We explore the feasibility of automatically finding accounts that publish sensitive content on Twitter. One natural approach to this problem is to first create a list of sensitive…