Stanford Security Lunch
Spring 2019

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April 03, 2019 Preventing Account Hijacking

Speaker:  Kurt Thomas

Abstract:  A recent survey from PEW found that—more than hate and harassment and more than misinformation—people are most concerned with the threat of identity theft and stolen data. This talk will provide a deep dive into the current landscape of account hijacking threats such as password washing, phishing, malware, and targeted operations and how best to protect users from each attack. We’ll show how underground markets provide a unique vantage point to measure the reach of attackers and their techniques. From these insights, we show why password-only authentication is obsolete, and how we’ve adopted a risk-aware, defense-in-depth approach to help keep users safe.

April 10, 2019 Private Communication without Synchronization

Speaker:  Saba Eskandarian

Abstract:  Rendezvous is a communication system that cryptographically protects metadata. Unlike all existing systems for metadata-hiding communication, Rendezvous does not require users to communicate in synchronous messaging rounds: Rendezvous provides meaningful metadata-hiding guarantees even if different users interact with the system at different rates. A Rendezvous deployment consists of a three-server cluster, and the system protects user privacy even if an active attacker controls one of the servers and any number of users. Every pair of Rendezvous users shares a secret virtual address that points to a unique mailbox stored at the servers. By cryptographically protecting accesses to virtual addresses, the honest servers prevent malicious servers and users from learning which mailbox has been updated when. By applying new cryptographic tools for detecting disruption attacks by malicious clients, Rendezvous reduces the bandwidth cost per message from O(√N) to O(logN) bits in an N-user deployment, which yields 4× and 8× overall performance improvements on the server and client sides, respectively, and reduces communication costs by one or more orders magnitude. Finally, we discuss how Rendezvous might apply in practice to protect communication between journalists and sources. This is joint work with Henry Corrigan-Gibbs, Matei Zharia, and Dan Boneh.

April 17, 2019 Measuring Abuse at Scale

Speaker:  David Freeman

Abstract:  The most difficult part of fighting abuse on a large consumer platform is not figuring out how to detect and block the bad guys — it's figuring out whether they're there in the first place. What's the “background level” of spam and fake accounts? How can we figure out what our detection systems are missing? Any metric that tries to answer these questions must have a number of properties: * It must be directionally correct — properly reflecting both new attacks and new interventions. * It must be actionable — able to be sliced up to surface specific examples. * It must avoid feedback loops — measure independently of what we've already found. * It must be robust to adversarial manipulation — decreases indicate a true drop in activity rather than adversaries avoiding the metric. * It must be scalable — able to adapt to new problems. In this talk I will present several approaches that Facebook's integrity teams have used to measure and prioritize their problems. I will discuss pros and cons of using user reports, human labeling, and automated labeling, and offer scenarios in which each of these should and shouldn't be used. Armed with these tools, you can go back to your product and find out exactly how much abuse it's attracting...the results could change your life!

April 24, 2019 No Lunch

May 01, 2019 Empowering Users to Make Privacy Decisions in Mobile Environments

Speaker:  Serge Egelman

Abstract:  Mobile platforms have enabled third-party app ecosystems that provide users with an endless supply of rich content. At the same time, mobile devices present very serious privacy risks: their ability to capture real-time data about our behaviors and preferences has created a marketplace for user data that most consumers are simply unaware of. In this talk, I will present prior and ongoing research that my group has performed to understand how users make privacy decisions on their mobile devices, including work that we have done to improve the usability of the permission-granting process through the use of machine learning. I will also present research that my research group has conducted to automatically examine the privacy behaviors of mobile apps. Using analysis tools that we developed, we have tested over 100,000 of the most popular Android apps to examine what data they access and with whom they share it. I will present data on how mobile apps are tracking and profiling users, how these practices are often against users' expectations and public disclosures, and how app developers may be violating various privacy regulations.

May 08, 2019 The Future of Securing Over the Air Automobile Updates using Uptane

Speaker:  Akshaykumar Mehta and Nupur Mehta

Abstract:  Software update systems for automobiles can deliver significant benefits, but, if not implemented carefully, they could potentially incur serious security vulnerabilities. Previous solutions for securing software updates consider standard attacks and deploy widely understood security mechanisms, such as digital signatures for the software updates, and hardware security modules (HSM) to sign software updates. However, no existing solution considers more advanced security objectives, such as resilience against a repository compromise, or freeze attacks to the vehicle’s update mechanism, or a compromise at a supplier’s site. Solutions developed for the PC world do not generalize to automobiles for two reasons: first, they do not solve problems that are unique to the automotive industry (e.g., that there are many different types of computers to be updated on a vehicle), and second, they do not address security attacks that can cause a vehicle to fail (e.g. a man-in-themiddle attack without compromising any signing key) or that can cause a vehicle to become unsafe. In this talk, we present Uptane, the first software update framework for automobiles that counters a comprehensive array of security attacks, and is resilient to partial compromises. Uptane adds strategic features to the state-of-the-art software update framework, TUF, in order to address automotive specific vulnerabilities and limitations. Uptane is flexible and easy to adopt. Its design details were developed together with the main automotive industry stakeholders in the USA; including OTAinfo. OTAinfo provides AI to secure IoT devices from attackers during a data transfer and is modifying the Uptane design to make it compatible with all connected devices.

May 15, 2019 Fidelius: Protecting User Secrets from Compromised Browsers

Speaker:  Saba Eskandarian

Abstract:  Users regularly enter sensitive data, such as passwords, credit card numbers, or tax information, into the browser window. While modern browsers provide powerful client-side privacy measures to protect this data, none of these defenses prevent a browser compromised by malware from stealing it. In this work, we present Fidelius, a new architecture that uses trusted hardware enclaves integrated into the browser to enable protection of user secrets during web browsing sessions, even if the entire underlying browser and OS are fully controlled by a malicious attacker. Fidelius solves many challenges involved in providing protection for browsers in a fully malicious environment, offering support for integrity and privacy for form data, JavaScript execution, XMLHttpRequests, and protected web storage, while minimizing the TCB. Moreover, interactions between the enclave and the browser, the keyboard, and the display all require new protocols, each with their own security considerations. Finally, Fidelius takes into account UI considerations to ensure a consistent and simple interface for both developers and users. As part of this project, we develop the first open source system that provides a trusted path from input and output peripherals to a hardware enclave with no reliance on additional hypervisor security assumptions. These components may be of independent interest and useful to future projects. We implement and evaluate Fidelius to measure its performance overhead, finding that Fidelius imposes acceptable overhead on page load and user interaction for secured pages and has no impact on pages and page components that do not use its enhanced security features. Joint work with Jonathan Cogan, Sawyer Birnbaum, Peh Chang Wei Brandon, Dillon Franke, Forest Fraser, Gaspar Garcia, Eric Gong, Hung Nguyen, Taresh Sethi, Vishal Subbiah, Michael Backes, Giancarlo Pellegrino, and Dan Boneh.

May 22, 2019 Adversarial Robustness and Adversarial Training for Multiple Perturbations

Speaker:  Florian Tramer

Abstract:  In this talk, I'll discuss some recent advances in the field of adversarial machine learning, explaining our current understanding of adversarial examples and the techniques we have for defending against them. I'll then argue that current techniques, that defend models against one specific type of adversarial examples, fail to naturally compose as we try to achieve robustness to multiple types of perturbations simultaneously. I'll conclude by presenting a number of open problems towards (scalable) training of robust machine learning models.

May 29, 2019 Towards Formalizing the GDPR Notion of Singling Out

Speaker:  Aloni Cohen

Abstract:  There is a significant conceptual gap between legal and mathematical thinking around data privacy. The effect is uncertainty as to the which technical offerings adequately match expectations expressed in legal standards. The uncertainty is exacerbated by a litany of successful privacy attacks, demonstrating that traditional statistical disclosure limitation techniques often fall short of the sort of privacy envisioned by legal standards. We define predicate singling out, a new type of privacy attack intended to capture the concept of singling out appearing in the General Data Protection Regulation (GDPR). Informally, an adversary predicate singles out a dataset X using the output of a data release mechanism M(X) if it manages to a predicate p matching exactly one row in X with probability much better than a statistical baseline. A data release mechanism that precludes such attacks is secure against predicate singling out (PSO secure). We argue that PSO security is a mathematical concept with legal consequences. Any data release mechanism that purports to "render anonymous" personal data under the GDPR must be secure against singling out, and hence must be PSO secure. We then analyze PSO security, showing that it fails to self-compose. Namely, a combination of $omega(log n)$ exact counts, each individually PSO secure, enables an attacker to predicate single out. In fact, the composition of just two PSO secure mechanisms can fail to provide PSO security. Finally, we ask whether differential privacy and k-anonymity are PSO secure. Leveraging a connection to statistical generalization, we show that differential privacy implies PSO security. However, k-anonymity does not: there exists a simple and general predicate singling out attack under mild assumptions on the k-anonymizer and the data distribution.

June 05, 2019 Applied Cybersecurity and Privacy at Enterprise Scale

Speaker:  Michael Duff

Abstract:  Gain insight into the University's infosec and privacy programs from Stanford's Chief Information Security Officer (and Interim Chief Privacy Officer). This will be an interactive discussion about our successes, failures, challenges, opportunities, and plans for the year ahead, highlighting recent developments including our new bug bounty program and how we're going passwordless with Cardinal Key.