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1. PIVA: Privacy-Preserving Identity Verification Methods for Accountless Users via Private List Intersection and Variants

   https://doi.org/10.1007/978-3-031-70896-1_18  

   Hwang, Seoyeon; Jarecki, Stanislaw; Karl, Zane; van_Kempen, Elina; Tsudik, Gene (September 2024, Springer Nature Switzerland)

   Garcia-Alfaro, J; Kozik, R; Choraś, M; Katsikas, S (Ed.)

   Several prominent privacy regulation (e.g., CCPA and GDPR) require service providers to let consumers request access to, correct, or delete, their personal data. Compliance necessitates verification of consumer identity. This is not a problem for consumers who already have an account with a service provider since they can authenticate themselves via a successful account log-in. However, there are no such methods for accountless consumers, even though service providers routinely collect data about casual consumers, i.e., those without accounts. Currently, in order to access their collected data, accountless consumers are asked to provide Personally Identifiable Information (PII) to service providers, which is privacy-invasive. To address this problem, we propose PIVA: Privacy-Preserving Identity Verification for Accountless Users, a technique based on Private List Intersection (PLI) and its variants. First, we introduce PLI, a close relative of private set intersection (PSI), a well-known cryptographic primitive that allows two or more mutually suspicious parties to compute the intersection of their private input sets. PLI takes advantage of the (ordered and fixed) list structure of each party’s private set. As a result, PLI is more efficient than PSI. We also explore PLI variants: PLI-cardinality (PLI-CA), threshold-PLI (t-PLI), and threshold-PLI-cardinality (t-PLI-CA), all of which yield less information than PLI. These variants are progressively better suited for addressing the accountless consumer authentication problem. We prototype and compare its performance against techniques based on regular PSI and garbled circuits (GCs). Results show that proposed PLI and PLI-CA constructions are more efficient than GC-based techniques, in terms of both computation and communication overheads. While GC-based t-PLI and t-PLI-CA execute faster, proposed constructs greatly outperform the former in terms of bandwidth, e.g., our t-PLI protocol consumes less bandwidth. We also show that proposed protocols can be made secure against malicious adversaries, with only moderate increases in overhead. These variants outperform their GC-based counterparts by at least one order of magnitude. 

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2. Brief Announcement: Federated Code Auditing and Delivery for MPC

   https://doi.org/10.1007/978-3-319-69084-1_20  

   Jansen, Frederick; Dak Albab, Kinan; Lapets, Andrei; Varia, Mayank (November 2017, SSS 2017: Stabilization, Safety, and Security of Distributed Systems)

   Secure multi-party computation (MPC) is a cryptographic primitive that enables several parties to compute jointly over their collective private data sets. MPC’s objective is to federate trust over several computing entities such that a large threshold (e.g., a majority) must collude before sensitive or private input data can be breached. Over the past decade, several general and special-purpose software frameworks have been developed that provide data contributors with control over deciding whom to trust to perform the calculation and (separately) to receive the output. However, one crucial component remains centralized within all existing MPC frameworks: the distribution of the MPC software application itself. For desktop applications, trust in the code must be determined once at download time. For web-based JavaScript applications subject to trust on every use, all data contributors across several invocations of MPC must maintain centralized trust in a single code delivery service. In this work, we design and implement a federated code delivery mechanism for web-based MPC such that data contributors only execute code that has been accredited by several trusted auditors (the contributor aborts if consensus is not reached). Our client-side Chrome browser extension is independent of any MPC scheme and has a trusted computing base of fewer than 100 lines of code. 

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3. Utilizing Keystroke Dynamics as Additional Security Measure to Protect Account Recovery Mechanism [Utilizing Keystroke Dynamics as Additional Security Measure to Protect Account Recovery Mechanism]

   https://doi.org/10.5220/0010191200330042  

   Wahab, Ahmed; Hou, Daqing; Schuckers, Stephanie; Barbir, Abbie (January 2021, 7th International Conference on Information Systems Security and Privacy)

   Account recovery is ubiquitous across web applications but circumvents the username/password-based login step. Therefore, it deserves the same level of security as the user authentication process. A common simplistic procedure for account recovery requires that a user enters the same email used during registration, to which a password recovery link or a new username could be sent. Therefore, an impostor with access to a user’s registration email and other credentials can trigger an account recovery session to take over the user’s account. To prevent such attacks, beyond validating the email and other credentials entered by the user, our proposed recovery method utilizes keystroke dynamics to further secure the account recovery mechanism. Keystroke dynamics is a type of behavioral biometrics that uses the analysis of typing rhythm for user authentication. Using a new dataset with over 500,000 keystrokes collected from 44 students and university staff when they fill out an account recovery web form of multiple fields, we have evaluated the performance of five scoring algorithms on individual fields as well as feature-level fusion and weighted-score fusion. We achieve the best EER of 5.47% when keystroke dynamics from individual fields are used, 0% for a feature-level fusion of five fields, and 0% for a weighted-score fusion of seven fields. Our work represents a new kind of keystroke dynamics that we would like to call it ‘medium fixed-text’ as it sits between the conventional (short) fixed text and (long) free text research. 

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4. Delphi: A Cryptographic Inference System for Neural Networks

   https://doi.org/10.1145/3411501.3419418  

   Mishra, Pratyush; Lehmkuhl, Ryan; Srinivasan, Akshayaram; Zheng, Wenting; Popa, Raluca Ada (November 2020, PPMLP'20: Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice)

   null (Ed.)

   Many companies provide neural network prediction services to users for a wide range of applications. However, current prediction systems compromise one party's privacy: either the user has to send sensitive inputs to the service provider for classification, or the service provider must store its proprietary neural networks on the user's device. The former harms the personal privacy of the user, while the latter reveals the service provider's proprietary model. We design, implement, and evaluate Delphi, a secure prediction system that allows two parties to execute neural network inference without revealing either party's data. Delphi approaches the problem by simultaneously co-designing cryptography and machine learning. We first design a hybrid cryptographic protocol that improves upon the communication and computation costs over prior work. Second, we develop a planner that automatically generates neural network architecture configurations that navigate the performance-accuracy trade-offs of our hybrid protocol. Together, these techniques allow us to achieve a 22x improvement in online prediction latency compared to the state-of-the-art prior work. 

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5. Unfiltered: Measuring Cloud-based Email Filtering Bypasses

   https://doi.org/10.1145/3589334.3645499  

   Rao, Sumanth; Liu, Enze; Ho, Grant; Voelker, Geoffrey M; Savage, Stefan (May 2024, Proceedings of the 2024 ACM Web Conference)

   Email service has increasingly been outsourced to cloud-based providers and so too has the task of filtering such messages for potential threats. Thus, customers will commonly direct that their incoming email is first sent to a third-party email filtering service (e.g., Proofpoint or Barracuda) and only the "clean" messages are then sent on to their email hosting provider (e.g., Gmail or Microsoft Exchange Online). However, this loosely coupled approach can, in theory, be bypassed if the email hosting provider is not configured to only accept messages that arrive from the email filtering service. In this paper we demonstrate that such bypasses are commonly possible. We document a multi-step methodology to infer if an organization has correctly configured its email hosting provider to guard against such scenarios. Then, using an empirical measurement of edu and com domains as a case study, we show that 80% of such organizations making use of popular cloud-based email filtering services can be bypassed in this manner. We also discuss reasons that lead to such misconfigurations and outline challenges in hardening the binding between email filtering and hosting providers. 

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