Accepted papers for PETS 2021

Issue 1

  • SGX-MR: Regulating Dataflows for Protecting Access Patterns of Data-Intensive SGX Applications
    A K M Mubashwir Alam (Marquette University), Sagar Sharma (HP Inc), and Keke Chen (Marquette University)
  • Controlled Functional Encryption Revisited: Multi-Authority Extensions and Efficient Schemes for Quadratic Functions
    Miguel Ambrona (NTT Secure Platform Laboratories), Dario Fiore (IMDEA Software Institute), and Claudio Soriente (NEC Laboratories Europe)
  • Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine Learning
    Valentino Rizzo (Ermes Cyber Security SRL), Stefano Traverso (Ermes Cyber Security SRL), and Marco Mellia (Politecnico di Torino)
  • Differential Privacy at Risk: Bridging Randomness and Privacy Budget
    Ashish Dandekar (École Normale Supériure, Paris, France), Debabrota Basu (Chalmers University of Technology, Gothenberg, Sweden), Stéphane Bressan (National University of Singapore, Singapore), and Ashish Dandekar (École Normale Supérieure)
  • On the (Im)Practicality of Adversarial Perturbation for Image Privacy
    Arezoo Rajabi (Oregon State University), Rakesh B. Bobba (Oregon State University), Mike Rosulek (Oregon State University), Charles V. Wright (Portland State University), and Wu-chi Feng (Portland State University)
  • SoK: Privacy-Preserving Reputation Systems
    Stan Gurtler (University of Waterloo) and Ian Goldberg (University of Waterloo)
  • Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping
    Jaewoo Lee (University of Georgia) and Daniel Kifer (Penn State University)
  • Real-time Analysis of Privacy-(un)aware IoT Applications
    Leonardo Babun (Florida International University), Z. Berkay Celik (Purdue University), Patrick McDaniel (Pennsylvania Sate University), and A. Selcuk Uluagac (Florida International University)
  • Secure Training of Decision Trees with Continuous Attributes
    Mark Abspoel (CWI, The Netherlands), Daniel Escudero (Aarhus University, Denmark), and Nikolaj Volgushev (Pleo Technologies ApS)
  • Falcon: Honest-Majority Maliciously Secure Framework for Private Deep Learning
    Sameer Wagh (Princeton University), Shruti Tople (Microsoft Research), Fabrice Benhamouda (Algorand Foundation), Eyal Kushilevitz (Technion), Prateek Mittal (Princeton University), and Tal Rabin (Algorand Foundation)
  • The Audio Auditor: User-Level Membership Inference in Internet of Things Voice Services
    Yuantian Miao (Swinburne University of Technology), Minhui Xue (The University of Adelaide), Chao Chen (Swinburne University of Technology), Lei Pan (Deakin University), Jun Zhang (Swinburne University of Technology), Benjamin Zi Hao Zhao (The University of New South Wales and CSIRO-Data61), Dali Kaafar (Macquarie University and CSIRO-Data61), and Yang Xiang (Swinburne University of Technology)
  • SoK: Managing Longitudinal Privacy of Publicly Shared Personal Online Data
    Theodor Schnitzler (Ruhr-Universität Bochum), Shujaat Mirza (Courant Institute of Mathematical Sciences, New York University), Markus Dürmuth (Ruhr-Universität Bochum), and Christina Pöpper (New York University Abu Dhabi)
  • Issue 2

  • Automated Extraction and Presentation of Data Practices in Privacy Policies
    Duc Bui (University of Michigan), Kang G. Shin (University of Michigan), Jongmin Choi (Samsung Research), and Jun Bum Shin (Samsung Research)
  • Scalable Privacy-Preserving Distributed Learning
    David Froelicher (EPFL), Juan R. Troncoso-pastoriza (EPFL), Apostolos Pyrgelis (EPFL), Sinem Sav (EPFL), Joao Sa Sousa (EPFL), Jean-Philippe Bossuat (EPFL), and Jean-Pierre Hubaux (EPFL)
  • Face-Off: Adversarial Face Obfuscation
    Varun Chandrasekaran (University of Wisconsin-Madison), Chuhan Gao (Microsoft), Brian Tang (University of Wisconsin-Madison), Kassem Fawaz (University of Wisconsin-Madison), Somesh Jha (University of Wisconsin-Madison), and Suman Banerjee (University of Wisconsin-Madison)
  • Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces with Cluster-Specific Features
    Takao Murakami (AIST), Koki Hamada (NTT), Yusuke Kawamoto (AIST), and Takuma Hatano (NSSOL)
  • "Warn Them" or "Just Block Them"?: Comparing Privacy Concerns of Older and Working Age Adults
    Hirak Ray (University of Maryland, Baltimore County), Flynn Wolf (University of Maryland, Baltimore County), Ravi Kuber (University of Maryland, Baltimore County), and Adam J. Aviv (The George Washington University)
  • Website Fingerprinting in the Age of QUIC
    Jean-Pierre Smith (ETH Zurich), Adrian Perrig (ETH Zurich), and Prateek Mittal (Princeton)
  • EL PASSO: Efficient and Lightweight Privacy-preserving Single Sign On
    Zhiyi Zhang (UCLA), Michał Król (City, University of London), Alberto Sonnino (UCL), Lixia Zhang (UCLA), and Etienne Riviere (UCLouvain)
  • Efficient homomorphic evaluation of k-NN classifiers
    Martin Zuber (CEA, LIST) and Renaud Sirdey (CEA, LIST)
  • Defending Against Microphone-Based Attacks with Personalized Noise
    Yuchen Liu (Indiana University Bloomington), Ziyu Xiang (Indiana University Bloomington), EJ Seong (Indiana University Bloomington), Apu Kapadia (Indiana University Bloomington), and Donald Williamson (Indiana University Bloomington)
  • Holes in the Geofence: Privacy Vulnerabilities in "Smart" DNS Services
    Rahel A. Fainchtein (Georgetown University), Adam A. Aviv (The George Washington University), Micah Sherr (Georgetown University), Stephen Ribaudo (Georgetown University), and Armaan Khullar (Georgetown University)
  • Too Close for Comfort: Morasses of (Anti-) Censorship in the Era of CDNs
    Devashish Gosain (IIIT Delhi), Mayank Mohindra (IIIT Delhi), and Sambuddho Chakravarty (IIIT Delhi)
  • Privacy-Preserving & Incrementally-Deployable Support for Certificate Transparency in Tor
    Rasmus Dahlberg (Karlstad University), Tobias Pulls (Karlstad University), Tom Ritter (Mozilla), and Paul Syverson (U.S. Naval Research Laboratory)
  • DyPS: Dynamic, Private and Secure GWAS
    Túlio Pascoal (SnT, University of Luxembourg), Jérémie Decouchant (SnT, University of Luxembourg), Antoine Boutet (Insa-Lyon, CITI, Inria), and Paulo Esteves-Verissimo (SnT, University of Luxembourg)
  • Validity and Reliability of the Scale Internet Users’ Information Privacy Concerns (IUIPC)
    Thomas Gross (Newcastle University)
  • A calculus of tracking: theory and practice
    Giorgio Di Tizio (University of Trento) and Fabio Massacci (University of Trento)
  • "Did you know this camera tracks your mood?": Modeling People's Privacy Expectations and Preferences in the Age of Video Analytics
    Shikun Zhang (Carnegie Mellon University), Yuanyuan Feng (Carnegie Mellon University), Lujo Bauer (Carnegie Mellon University), Lorrie Cranor (Carnegie Mellon University), Anupam Das (North Carolina State University), and Norman Sadeh (Carnegie Mellon University)
  • GANDaLF: GAN for Data-Limited Fingerprinting
    Se Eun Oh (University of Minnesota), Nate Mathews (Rochester Institute of Technology), Mohammad Saidur Rahman (Rochester Institute of Technology), Matthew Wright (Rochester Institute of Technology), and Nicholas Hopper (University of Minnesota)
  • Revisiting Membership Inference Under Realistic Assumptions
    Bargav Jayaraman (University of Virginia), Lingxiao Wang (University of California Los Angeles), Katherine Knipmeyer (University of Virginia), Quanquan Gu (University of California Los Angeles), and David Evans (University of Virginia)
  • Déjà vu: Abusing Browser Cache Headers to Identify and Track Online Users
    Vikas Mishra (Inria / Univ. Lille), Pierre Laperdrix (CNRS / Univ. Lille / Inria), Walter Rudametkin (Univ. Lille / Inria), and Romain Rouvoy (Univ. Lille / Inria)
  • Issue 3

  • Exploring Mental Models of the Right to Informational Self-Determination of Office Workers in Germany
    Jan Tolsdorf (Hochschule Bonn-Rhein-Sieg University of Applied Sciences), Florian Dehling (Hochschule Bonn-Rhein-Sieg University of Applied Sciences), Delphine Reinhardt (University of Göttingen), and Luigi Lo Iacono (Hochschule Bonn-Rhein-Sieg University of Applied Sciences)
  • Genome Reconstruction Attacks Against Genomic Data-Sharing Beacons
    Kerem Ayoz (Bilkent University), Erman Ayday (Case Western Reserve University), and Ercument Cicek (Bilkent University)
  • The Motivated Can Encrypt (Even with PGP)
    Glencora Borradaile (Oregon State University), Kelsy Kretschmer (Oregon State University), Michele Gretes (Stand), and Alexandria LeClerc (Oregon State University)
  • Three Years Later: A Study of MAC Address Randomization In Mobile Devices And When It Succeeds
    Ellis Fenske (US Naval Academy), Dane Brown (US Naval Academy), Jeremy Martin (MITRE), Travis Mayberry (US Naval Academy), Peter Ryan, Jr. (MITRE), and Erik Rye (CMAND)
  • SoK: Privacy-Preserving Collaborative Tree-based Model Learning
    Sylvain Chatel (EPFL), Apostolos Pyrgelis (EPFL), Juan Ramón Troncoso-Pastoriza (EPFL), and Jean-Pierre Hubaux (EPFL)
  • FoggySight: A Scheme for Facial Lookup Privacy
    Ivan Evtimov (University of Washington), Pascal Sturmfels (University of Washington), and Tadayoshi Kohno (University of Washington)
  • Who Can Find My Devices? Security and Privacy of Apple's Crowd-Sourced Bluetooth Location Tracking System
    Milan Stute (Technical University of Darmstadt), Alexander Heinrich (Technical University of Darmstadt), Tim Kornhuber (Technical University of Darmstadt), and Matthias Hollick (Technical University of Darmstadt)
  • Faster homomorphic comparison operations for BGV and BFV
    Ilia Iliashenko (imec-COSIC, KU Leuven, Belgium) and Vincent Zucca (Équipe DALI, Université de Perpignan via Domitia, France; LIRMM, UMR 5506, Université de Montpellier, CNRS, France)
  • Foundations of Ring Sampling
    Viktoria Ronge (Friedrich-Alexander University Erlangen-Nuremberg), Christoph Egger (Friedrich-Alexander University Erlangen-Nuremberg), Russell W. F. Lai (Friedrich-Alexander University Erlangen-Nuremberg), Dominique Schröder (Friedrich-Alexander University Erlangen-Nuremberg), and Hoover H. F. Yin (The Chinese University of Hong Kong)
  • Fast Privacy-Preserving Punch Cards
    Saba Eskandarian (Stanford University)
  • Awareness, Adoption, and Misconceptions of Web Privacy Tools
    Peter Story (Carnegie Mellon University), Daniel Smullen (Carnegie Mellon University), Yaxing Yao (Carnegie Mellon University), Alessandro Acquisti (Carnegie Mellon University), Lorrie Faith Cranor (Carnegie Mellon University), Norman Sadeh (Carnegie Mellon University), and Florian Schaub (University of Michigan)
  • The Role of Privacy in Digitalization – Analyzing Perspectives of German Farmers
    Sebastian Linsner (Technical University of Darmstadt), Franz Kuntke (Technical University of Darmstadt), Enno Steinbrink (Technical University of Darmstadt), Jonas Franken (Technical University of Darmstadt), Christian Reuter (Technical University of Darmstadt), and Sebastian Linsner (Technical University of Darmstadt)
  • Data Portability between Online Services: An Empirical Analysis on the Effectiveness of GDPR Art. 20
    Emmanuel Syrmoudis (Technical University of Munich), Stefan Mager (Ludwig-Maximilians-University of Munich), Sophie Kuebler-Wachendorff (Ludwig-Maximilians-University of Munich), Paul Pizzinini (Ludwig-Maximilians-University of Munich), Jens Grossklags (Technical University of Munich), and Johann Kranz (Ludwig-Maximilians-University of Munich)
  • Digital inequality through the lens of self-disclosure
    Jooyoung Lee (The Pennsylvania State University), Sarah Rajtmajer (The Pennsylvania State University), Eesha Srivatsavaya (The Pennsylvania State University), and Shomir Wilson (The Pennsylvania State University)
  • The CNAME of the Game: Large-scale Analysis of DNS-based Tracking Evasion
    Yana Dimova (imec-DistriNet, KU Leuven), Gunes Acar (imec-COSIC, KU Leuven), Lukasz Olejnik (European Data Protection Supervisor), Wouter Joosen (imec-DistriNet, KU Leuven), and Tom Van Goethem (imec-DistriNet, KU Leuven)
  • DNA Sequencing Flow Cells and the Security of the Molecular-Digital Interface
    Peter Ney (University of Washington), Lee Organick (University of Washington), Jeff Nivala (University of Washington), Luis Ceze (University of Washington), and Tadayoshi Kohno (University of Washington)
  • A First Look at Private Communications in Video Games using Visual Features
    Abdul Wajid (SEECS, NUST), Nasir Kamal (SEECS, NUST), Muhammad Sharjeel (SEECS, NUST), Raaez Muhammad Sheikh (SEECS, NUST), Huzaifah Bin Wasim (SEECS, NUST), Muhammad Hashir Ali (SEECS, NUST), Wajahat Hussain (SEECS, NUST), Syed Taha Ali (SEECS, NUST), and Latif Anjum (SEECS, NUST)
  • ML-CB: Machine Learning Canvas Block
    Nathan Reitinger (UMD) and Michelle L. Mazurek (UMD)
  • Defining Privacy: How Users Interpret Technical Terms in Privacy Policies
    Jenny Tang (Wellesley College), Hannah Shoemaker (Pomona College), Ada Lerner (Wellesley College), and Eleanor Birrell (Pomona College)
  • Unlinkable Updatable Hiding Databases and Privacy-Preserving Loyalty Programs
    Aditya Damodaran (University of Luxembourg) and Alfredo Rial (University of Luxembourg)
  • Growing synthetic data through differentially-private vine copulas
    Sébastien Gambs (UQAM), Frédéric Ladouceur (Ericsson), Antoine Laurent (UQAM), and Alexandre Roy-Gaumond (UQAM)
  • privGAN: Protecting GANs from membership inference attacks at low cost to utility
    Sumit Mukherjee (Microsoft), Yixi Xu (Microsoft), Anusua Trivedi (Microsoft), Nabajyoti Patowary (Microsoft), Juan Lavista Ferres (Microsoft)
  • Issue 4

  • SoK: Efficient Privacy-preserving Clustering
    Aditya Hegde (International Institute of Information Technology Bangalore), Helen Möllering (Technical University of Darmstadt), Thomas Schneider (Technical University of Darmstadt), and Hossein Yalame (Technical University of Darmstadt)
  • Fortified Multi-Party Computation: Taking Advantage of Simple Secure Hardware Modules
    Brandon Broadnax, Alexander Koch (Karlsruhe Institute of Technology), Jeremias Mechler (Karlsruhe Institute of Technology), Tobias Müller (FZI Research Center for Information Technology), Jörn Müller-Quade (Karlsruhe Institute of Technology), and Matthias Nagel
  • Secure integer division with a private divisor
    Mark Abspoel (CWI) and Thijs Veugen (TNO)
  • CrowdNotifier: Decentralized Privacy-Preserving Presence Tracing
    Wouter Lueks (EPFL), Seda Gürses (TU Delft), Michael Veale (UCL), Edouard Bugnion (EPFL), Marcel Salathé (EPFL), Kenneth G. Paterson (ETHZ), and Carmela Troncoso (EPFL)
  • Blocking Without Breaking: Identification and Mitigation of Non-Essential IoT Traffic
    Anna Maria Mandalari (Imperial College London), Daniel J. Dubois (Northeastern University), Roman Kolcun (Imperial College London), Muhammad Talha Paracha (Northeastern University), Hamed Haddadi (Imperial College London), and David Choffnes (Northeastern University)
  • Residue-Free Computing
    Logan Arkema (Georgetown University) and Micah Sherr (Georgetown University)
  • Differentially Private Naive Bayes Classifier using Smooth Sensitivity
    Farzad Zafarani (Purdue University) and Chris Clifton (Purdue University)
  • Supervised Authorship Segmentation of Open Source Code Projects
    Edwin Dauber (Drexel University), Robert Erbacher (U.S. Army Research Laboratory), Gregory Shearer (ICF International), Michael Weisman (U.S. Army Research Laboratory), Frederica Nelson (U.S. Army Research Laboratory), and Rachel Greenstadt (New York University)
  • Gage MPC: Bypassing Residual Function Leakage for Non-Interactive MPC
    Ghada Almashaqbeh (University of Connecticut), Fabrice Benhamouda (Algorand Foundation), Seungwook Han (Columbia University), Daniel Jaroslawicz (Columbia University), Tal Malkin (Columbia University), Alex Nicita (Columbia University), Tal Rabin (UPenn University and Algorand Foundation), Abhishek Shah (Columbia University), and Eran Tromer (Columbia University and Tel Aviv University)
  • Privacy-Preserving Approximate k-Nearest-Neighbors Search that Hides Access, Query and Volume Patterns
    Alexandra Boldyreva (Georgia Institute of Technology) and Tianxin Tang (Georgia Institute of Technology)
  • DPlis: Boosting Utility of Differentially Private Deep Learning via Randomized Smoothing
    Wenxiao Wang (Tsinghua university), Tianhao Wang (Harvard University), Lun Wang (University of California, Berkeley), Nanqing Luo (Huazhong University of Science and Technology), Pan Zhou (Huazhong University of Science and Technology), Dawn Song (University of California, Berkeley), and Ruoxi Jia (Virginia Tech)
  • LogPicker: Strengthening Certificate Transparency against covert adversaries
    Alexandra Dirksen (TU Braunschweig), David Klein (TU Braunschweig), Robert Michael (TU Braunschweig), Tilman Stehr (), Konrad Rieck (TU Braunschweig), and Martin Johns (TU Braunschweig)
  • "We, three brothers have always known everything of each other": A Cross-cultural Study of Sharing Digital Devices and Online Accounts
    Mahdi Nasrullah Al-Ameen (Utah State University), Huzeyfe Kocabas (Utah State University), Swapnil Nandy (Jadavpur University), and Tanjina Tamanna (University of Dhaka)
  • Privacy Preference Signals: Past, Present and Future
    Maximilian Hils (University of Innsbruck), Daniel Woods (University of Innsbruck), Rainer Böhme (University of Innsbruck), and Maximilian Hils (University of Innsbruck)
  • SwapCT: Swap Confidential Transactions for Privacy-Preserving Multi-Token Exchanges
    Felix Engelmann (Aarhus University), Lukas Müller (Ulm University), Andreas Peter (University of Twente), Frank Kargl (Ulm University), and Christoph Bösch (Ulm University)
  • Multiparty Homomorphic Encryption from Ring-Learning-With-Errors
    Christian Mouchet (EPFL), Juan Troncoso-Pastoriza (EPFL), Jean-Philippe Bossuat (EPFL), and Jean-Pierre Hubaux (EPFL)
  • Domain Name Encryption Is Not Enough: Privacy Leakage via IP-based Website Fingerprinting
    Nguyen Phong Hoang (Stony Brook University), Arian Akhavan Niaki (University of Massachusetts, Amherst), Phillipa Gill (University of Massachusetts, Amherst), and Michalis Polychronakis (Stony Brook University)
  • Mercurial Signatures for Variable-Length Messages
    Elizabeth C. Crites (University College London) and Anna Lysyanskaya (Brown University)
  • Unifying Privacy Policy Detection
    Henry Hosseini (University of Münster), Martin Degeling (Ruhr University Bochum), Christine Utz (Ruhr University Bochum), and Thomas Hupperich (University of Münster)
  • Managing Potentially Intrusive Practices In The Browser: A User-Centered Perspective
    Daniel Smullen (Carnegie Mellon University), Yaxing Yao (University of Maryland, Baltimore County), Yuanyuan Feng (Carnegie Mellon University), Norman Sadeh (Carnegie Mellon University), Arthur Edelstein (Mozilla), and Rebecca Weiss (Mozilla)
  • Oblivious DNS over HTTPS (ODoH): A Practical Privacy Enhancement to DNS
    Sudheesh Singanamalla (University of Washington / Cloudflare Inc.), Suphanat Chunhapanya (Cloudflare Inc.), Jonathan Hoyland (Cloudflare Inc.), Marek Vavruša (Cloudflare Inc.), Tanya Verma (Cloudflare Inc.), Peter Wu (Cloudflare Inc.), Marwan Fayed (Cloudflare Inc.), Kurtis Heimerl (University of Washington), Nick Sullivan (Cloudflare Inc.), and Christopher Wood (Cloudflare Inc.)
  • zkSENSE: A Friction-less Privacy-Preserving HumanAttestation Mechanism for Mobile Devices
    Iñigo Querejeta-Azurmendi (Universidad Carlos III Madrid / ITFI CSIC), Panagiotis Papadopoulos (Telefonica), Matteo Varvello (Nokia Bell Labs), Antonio Nappa (University of California, Berkeley), Jiexin Zhang (Cambridge University), and Benjamin Livshits (Brave Software / Imperial College London)
  • You May Also Like... Privacy: Recommendation Systems Meet PIR
    Adithya Vadapalli (Indiana University), Fattaneh Bayatbabolghani (UC Berkeley), and Ryan Henry (University of Calgary)
  • "I would have to evaluate their objections": Privacy tensions between smart home device owners and incidental users
    Camille Cobb (Carnegie Mellon University), Sruti Bhagavatula (Carnegie Mellon University), Kalil Anderson Garrett (Carnegie Mellon University), Alison Hoffman (Carnegie Mellon University), Varun Rao (Carnegie Mellon University), and Lujo Bauer (Carnegie Mellon University)
  • HashWires: Hyperefficient Credential-Based Range Proofs
    Konstantinos Chalkias (Facebook / Novi), Shir Cohen (Technion), Kevin Lewi (Facebook / Novi), Fredric Moezinia (Facebook / Novi), and Yolan Romailler (Facebook / Novi)
  • Less is More: A privacy-respecting Android malware classifier using federated learning
    Rafa Gálvez (imec-COSIC ESAT/KU Leuven), Veelasha Moonsamy (Ruhr University Bochum), and Claudia Diaz (imec-COSIC ESAT/KU Leuven)
  • Private Stream Aggregation with Labels in the Standard Model
    Johannes Ernst (University St. Gallen) and Alexander Koch (Karlsruhe Institute of Technology)
  • SoK: Privacy-Preserving Computation Techniques for Deep Learning
    José Cabrero-Holgueras (CERN) and Sergio Pastrana (Universidad Carlos III de Madrid)