Sauvik Das
Associate Professor
Studies usable privacy and security, including how people perceive privacy risks of AI.
Sauvik Das's lab at CMU works on interfaces & interaction techniques. 32 papers since 2023, 19 at top venues, mostly CHI and CSCW. 4 PhD students. 3 alumni. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 59%
- PhD students
- 4
- MS students
- 0
- alumni listed
- 3
Papers by yearfrom the lab homepage, 2023–2026
- 6 papers2026 · T1 4 · collecting
- 11 papers2025 · T1 6
- 8 papers2024 · T1 7
- 7 papers2023 · T1 2
T1 venuesOtherVenues: CHI, CSCW, FAccT, AAAI/ACM AIES, UIST
Research focuspaper titles that use each topic's words
- privacy15 papers
- risks8 papers
- perceive1 paper
- Interfaces & interaction techniques0 papers
- people perceive0 papers
Paper list
From the lab homepage.
20266 papers
- ICLR (T1)Do Vision-Language Models Respect Contextual Integrity in Location Disclosure? (opens in a new tab)
- CHI (T1)Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Concepts (opens in a new tab)
- CHI (T1)Supporting Informed Self-Disclosure: Design Recommendations for Presenting AI-Estimates of Privacy Risks to Users (opens in a new tab)
- AAAI/ACM AIESThe Perils of Agency: How Developers Perceive, Prioritize, and Address Risks in Agentic AI Products (opens in a new tab)
- UIST (T1)WhatIf: Interactive Exploration of Large-Scale LLM Agent Simulations for Policy Reasoning (opens in a new tab)
- CAISWhy Johnny Can’t Use Agents: Industry Aspirations vs. User Realities with AI Agents (opens in a new tab)
202511 papers
- FAccTActions Speak Louder Than Words: Agent Decisions Reveal Implicit Biases in Language Models (opens in a new tab)
- CSCW (T1)Design(ing) Fictions for Collective Civic Reporting of Privacy Harms (opens in a new tab)
- UIST (T1)Imago Obscura: An Image Privacy AI Co-pilot to Enable Identification and Mitigation of Risks (opens in a new tab)
- CSCW (T1)Measuring, Modeling, and Helping People Account for Privacy Risks in Online Self-Disclosures with AI (opens in a new tab)
- USECModeling End-User Affective Discomfort With Mobile App Permissions Across Physical Contexts (opens in a new tab)
- CHI (T1)Of Secrets and Seedphrases: Conceptual Misunderstandings and Security Challenges for Seed Phrase Management among Cryptocurrency Users (opens in a new tab)
- USECOn-demand RFID: Improving Privacy, Security, and User Trust in RFID Activation through Physically-Intuitive Design (opens in a new tab)
- NeurIPS (T1)Probabilistic Reasoning with LLMs for Privacy Risk Estimation (opens in a new tab)
- TOCHI (T1)Purpose Mode: Reducing Distraction Through Toggling Attention Capture Damaging Patterns on Social Media Websites (opens in a new tab)
- AAAI/ACM AIESSound Check: Auditing Recent Audio Dataset Practices (opens in a new tab)
- NSPWTransformational Provocations for Usable Privacy and Security: Designing Beyond Compliance and Expert Norms (opens in a new tab)
20248 papers
- CHI (T1)“Don’t Put All Your Eggs In One Basket”: How Cryptocurrency Users Choose and Secure Their Wallets (opens in a new tab)
- CHI (T1)"It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents (opens in a new tab)
- FAccTA Systematic Review of Biometric Monitoring in the Workplace: Analyzing Socio-technical Harms in Development, Deployment and Use (opens in a new tab)
- CHI (T1)Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Risks (opens in a new tab)
- EMNLP (T1)Granular Privacy Control for Geolocation with Vision Language Models (opens in a new tab)
- USENIX Security (T1)I Don’t Know If We’re Doing Good. I Don’t Know If We’re Doing Bad”: Investigating How Practitioners Scope, Motivate, and Conduct Privacy Work When Developing AI Products (opens in a new tab)
- ACL (T1)Reducing Privacy Risks in Online Self-Disclosures with Language Models (opens in a new tab)
- CSCW (T1)The Subversive AI Acceptance Scale (SAIA-8): A Scale to Measure User Acceptance of AI-Generated, Privacy-Enhancing Image Modifications (opens in a new tab)
20237 papers
- SOUPSGuardLens: Supporting Safer Online Browsing for People with Visual Impairments (opens in a new tab)
- SOUPSImageAlly: A Human-AI Hybrid Approach to Support Blind People in Detecting and Redacting Private Image Content (opens in a new tab)
- SOUPSIterative Design of An Accessible CryptoWallet for Blind Users (opens in a new tab)
- USENIX Security (T1)Powering for Privacy: Improving User Trust in Smart Speaker Microphones with Intentional Powering and Perceptible Assurance (opens in a new tab)
- CACMPrivacy in the Age of AI: What's changed and what should we do about it? (opens in a new tab)
- FAccTThe Slow Violence of Surveillance Capitalism: How Online Behavioral Advertising Harms People (opens in a new tab)
- S&P (T1)When and Why Do People Want Ad Targeting Explanations? Evidence from a Four-Week, Mixed-Methods Field Study (opens in a new tab)
Where alumni went3 listed, 0% to industry
Industry 0Academia 1
Apply to CMUFall 2027
CMU SCS Computer Science PhD
- Round
- Single round
- Opens
- GRE
- See program page
- English
- TOEFL / IELTS / Duolingo (F-1/J-1 non-native speakers)
- Fee
- USD 100 (USD 80 by early deadline)
Confirmed on the program page.
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