PEPR '22
as the organisers put it
The 2022 USENIX Conference on Privacy Engineering Practice and Respect (PEPR '22) will take place at the Hyatt Regency Santa Clara, in Santa Clara, CA, USA, and also as a virtual event on June 23-24, 2022.
who it's for
privacy engineers, data scientists, software engineers, privacy researchers, security engineers, product managers, legal counsel
topics
differential privacy implementations; differentially private analytics platforms; python differential privacy pipelines; privacy threat modeling; privacy audit methodology; voice assistant consent design; cookie consent interface design; dns privacy protocols; mobile app privacy labels; privacy-preserving camera protocols; DP synthetic data and DP-SGD; identifying personal data with neural networks
| title | author | length |
|---|---|---|
| Negotiating Privacy/Utility Trade-Offs under Differential Privacy | Gerome Miklau | 17m |
The College Scorecard project links Department of Education cohorts to IRS earnings data and moves from ad hoc suppression to differential privacy, producing… | ||
| Publishing Wikipedia Project Usage Data with Strong Privacy Protections and w/o Tracking | Hal Triedman | 17m |
Wikimedia Foundation pilots releasing page-view counts by project and country with differential privacy on a 1.5–2 TB/day dataset, working with Tumult Labs… | ||
| Expanding Differentially Private Solutions: A Python Case Study | Vadym Doroshenko | 09m |
PipelineDP is a Python library that implements end-to-end differentially private pipeline primitives (counts, sums, joins and per-user contributions) and a… | ||
| Designing an Open-Source Platform for Differentially Private Analytics That Is Usable... | Michael Hay | 11m |
Tumult Analytics is an open‑source platform for configurable central differential privacy used by IRS, US Census Bureau, and Wikimedia that claims provable… | ||
| Compiling Python Programs into Differentially Private Ones | Johan Leduc, Nicolas Grislain | 10m |
A service ingests CSV or SQL data, computes differentially private marginals, and trains a DP generative model to produce synthetic data for offline iteration… | ||
| Privacy Threat Modeling | Cara Bloom | 22m |
Claims a gap in privacy risk models and presents a data‑driven hierarchical model built from 146 non‑breach privacy events mapped into clusters and generic… | ||
| Privacy Audits 101 | Lauren Reid | 12m |
Defines privacy audit as evidence-based, scope-limited testing of stated controls, not investigations or pen tests, and shows a real example: PwC audit of… | ||
| Privacy Design Flaws | Eivind Arvesen | 17m |
Examines concrete architecture mistakes: a contact‑tracing app that continuously sends location+Bluetooth to a government cloud, Netflix’s 500,000 ratings… | ||
| Privacy Shift Left: A Machine-Assisted Threat Modeling Approach | Kristen Tan | 20m |
Compares six open-source tools against concrete criteria—complexity of detection logic, amenability to custom threats, operational usability, security… | ||
| Consent on the Fly: Developing Ethical Verbal Consent for Voice Assistants | William Seymour | 09m |
Alexa's voice-forward consent lets users grant skills access mid-conversation but delegates control to the platform voice so third-party and system prompts… | ||
| Informing the Design of Cookie Consent Interfaces with Research | Lorrie Faith Cranor | 20m |
Research tests 12–14 banner variants on a mock ecommerce site with 1,100 participants and audits of 191 real sites (150 show unequal paths). Interfaces that… | ||
| Panel: DNS Privacy Vs. | Mallory Knodel, Shivan Kaul Sahib | 46m |
Private DNS deployments (DoT, DoH, DoQ, Oblivious DoH) actually change who sees IPs and queries: Apple ships ODoH in iCloud Private Relay so proxies and… | ||
| Helping Mobile App Developers Create Accurate Privacy Labels | Jack Gardner, Akshath Jain | 21m |
A tool called Privacy Label Wiz combines static analysis of iOS permissions with interactive reflection questions to help developers complete Apple’s privacy… | ||
| Creating Effective Labels | Shakthi Gopavaram | 11m |
Study builds modular privacy labels and tests them with an IoT marketplace and user experiments, reporting concrete effects: a willingness to accept framing… | ||
| Three Years of Crowdsourcing Network Traffic from Smart Homes | Danny Yuxing Huang | 18m |
An open-source tool called IoT Inspector collects three years of smart-home network metadata using ARP spoofing and crowdsourced labels, amassing traffic from… | ||
| Informing the Design of Privacy Awareness Mechanisms for Users and Bystanders... | Yaxing Yao | 11m |
Four prototypes—privacy dashboard, mobile app scanner, ambient light, and privacy speaker—are built and demoed and evaluated via a survey to compare users and… | ||
| Privacy-Preserving Protocols for Smart Camera Systems and Other IoT Devices | Yohan Beugin | 11m |
Design returns full data control to users by combining end-to-end encryption with a device bootstrap over physical pairing + out-of-band visual channel and an… | ||
| Data Privacy Vocabulary (DPV): Concepts for Legal Compliance | Harshvardhan J. Pandit | 08m |
A reusable vocabulary of machine-readable metadata for personal data handling that maps concrete concepts — data categories, purposes, processing operations… | ||
| Integrating Differential Privacy and Contextual Integrity | Sebastian Benthall | 09m |
Proposes a concrete hybrid: add a new transmission property parameter to contextual integrity and use it to choose differential privacy epsilon/delta by… | ||
| A Closer Look: Evaluating Location Privacy Empirically | Liyue Fan | 15m |
Empirical evaluation compares local online location-privacy methods on Geolife and Rio bus traces with 300m grid cells and 5‑minute sampling, measuring record… | ||
| Building a Privacy Testbed | Partha Das Chowdhury, Awais Rashid | 10m |
A reproducible lab emulates real-world networks to measure what apps actually leak: KVM-based virtualization runs thousands of guest VMs, a Rust CLI modeled on… | ||
| How Developers (Don't) Think about Gender Privacy | Elijah Bouma-Sims | 11m |
Study analyzes 900 posts with ~10,000 comments from Reddit plus code snippets and conducts 15 interviews (two-thirds recruited via Upwork, others via Twitter)… | ||
| Data Mapping at a Billion Dollar Self-Driving Vehicle Startup | Marc-Antoine Paré | 18m |
Cruise describes an automated data-mapping system that scans BigQuery schemas and sampled content across ~14 PB, millions of tables and tens of millions of… | ||
| Bringing Content Blocking to the Masses | Shivan Kaul Sahib, Anton Lazarev | 19m |
Brave brings filter-list blocking to ~50 million daily users by combining measurement-driven rule generation with engineering fixes and community support. A V8… | ||
| Lyft and the California Consumer Privacy Act | Shankar Garikapati, Alejo Grigera Sutro | 19m |
Lyft builds an event-driven, federated system on AWS for CCPA exports and erasures that goes live in early 2020 and serves a platform with ~18M riders. A… | ||
| Automating Product Deprecation | Will Shackleton | 18m |
Meta automates deprecation by running a dead-data cleanup system that scans production stores, notifies owners, blocks reads/writes after one month and then… | ||
| Identifying Personal Data by Fusing Elasticsearch with Neural Networks | Rakshit Wadhwa, Ryan Turner | 19m |
System converts column metadata into document lookups so Elasticsearch queries retrieve candidate labels and a small neural model acts as a neural-net… | ||
| Differentially Private Algorithms for 2020 Decennial Census Detailed DHC Race & Ethnicity | Samuel Haney, Rachel Marks | 15m |
Proposal produces DDHC race and ethnicity counts by adaptively choosing which breakdowns to release based on noisy group size, trading off accuracy and… | ||
| Data Structures for Data Privacy: Lessons Learned in Production | Rebecca Bilbro, Benjamin Bengfort | 22m |
Decentralized protocol implements a peer‑to‑peer PKI and a global directory yellow pages to mutually authenticate VASPs, uses gRPC/protobuf plus mTLS, and… | ||
| Panel: Fueling the Emerging Privacy Tech Industry | Lourdes M. Turrecha, Nishant Bhajaria | 42m |
Published a 2021 TROPT whitepaper and convened a ~20‑person working group to map an emerging privacy tech stack across the data and development lifecycles… | ||
| A Way Forward: What We Know (or Not) about CSAM & Privacy | Tatiana Renae Ringenberg, Lorraine G. Kisselburgh | 12m |
Reports show a 30% increase in child sexual abuse during COVID-19 and companies deploy three Apple measures: parental nudity filters, help guidance in… | ||
| Privacy Firefighting: Incident Management Lessons from (Literal) Fires | Katie Hufker | 09m |
Meta documents two concrete incidents—a stolen unencrypted payroll drive and a live stream where non-friends could comment—and treats vulnerabilities like… | ||
| Privacy and Respectful Discourse in AI Chatbots | Jayati Dev | 10m |
A thematic analysis of 37 conversational logs from Cleverbot finds users disclose sensitive data — especially health is the largest category (depression… | ||
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