123 citations · 443 across the 21 of their papers we have counts for
24 papers
MFDPG: Multi-Factor Authenticated Password Management With Zero Stored Secrets
Vivek Nair, Dawn Song
While password managers are a vital tool for internet security, they can also create a massive central point of failure, as evidenced by several major recent data breaches. For ove…
Multi-Factor Credential Hashing for Asymmetric Brute-Force Attack Resistance
Vivek Nair, Dawn Song
Since the introduction of bcrypt in 1999, adaptive password hashing functions, whereby brute-force resistance increases symmetrically with computational difficulty for legitimate u…
Decentralizing Custodial Wallets with MFKDF
Vivek Nair, Dawn Song
The average cryptocurrency user today faces a difficult choice between centralized custodial wallets, which are notoriously prone to spontaneous collapse, or cumbersome self-custod…
Inferring Private Personal Attributes of Virtual Reality Users from Head and Hand Motion Data
Vivek Nair, Christian Rack, Wenbo Guo +8
Motion tracking "telemetry" data lies at the core of nearly all modern virtual reality (VR) and metaverse experiences. While generally presumed innocuous, recent studies have demon…
Unique Identification of 50,000+ Virtual Reality Users from Head & Hand Motion Data
Vivek Nair, Wenbo Guo, Justus Mattern +4
With the recent explosive growth of interest and investment in virtual reality (VR) and the so-called "metaverse," public attention has rightly shifted toward the unique security a…
SoK: Data Privacy in Virtual Reality
Gonzalo Munilla Garrido, Vivek Nair, Dawn Song
The adoption of virtual reality (VR) technologies has rapidly gained momentum in recent years as companies around the world begin to position the so-called "metaverse" as the next…
Going Incognito in the Metaverse: Achieving Theoretically Optimal Privacy-Usability Tradeoffs in VR
Vivek Nair, Gonzalo Munilla Garrido, Dawn Song
Virtual reality (VR) telepresence applications and the so-called "metaverse" promise to be the next major medium of human-computer interaction. However, with recent studies demonst…
Multi-Factor Key Derivation Function (MFKDF) for Fast, Flexible, Secure, & Practical Key Management
Vivek Nair, Dawn Song
We present the first general construction of a Multi-Factor Key Derivation Function (MFKDF). Our function expands upon password-based key derivation functions (PBKDFs) with support…
Learning to Learn to Predict Performance Regressions in Production at Meta
Moritz Beller, Hongyu Li, Vivek Nair +6
Catching and attributing code change-induced performance regressions in production is hard; predicting them beforehand, even harder. A primer on automatically learning to predict p…
Exploring the Privacy Risks of Adversarial VR Game Design
Vivek Nair, Gonzalo Munilla Garrido, Dawn Song +1
Fifty study participants playtested an innocent-looking "escape room" game in virtual reality (VR). Within just a few minutes, an adversarial program had accurately inferred over 2…
Whence to Learn? Transferring Knowledge in Configurable Systems using BEETLE
Rahul Krishna, Vivek Nair, Pooyan Jamshidi +1
As software systems grow in complexity and the space of possible configurations increases exponentially, finding the near-optimal configuration of a software system becomes challen…
Is One Hyperparameter Optimizer Enough?
Huy Tu, Vivek Nair
Hyperparameter tuning is the black art of automatically finding a good combination of control parameters for a data miner. While widely applied in empirical Software Engineering, t…
Micky: A Cheaper Alternative for Selecting Cloud Instances
Chin-Jung Hsu, Vivek Nair, Tim Menzies +1
Most cloud computing optimizers explore and improve one workload at a time. When optimizing many workloads, the single-optimizer approach can be prohibitively expensive. Accordingl…
Transfer Learning with Bellwethers to find Good Configurations
Vivek Nair, Rahul Krishna, Tim Menzies +1
As software systems grow in complexity, the space of possible configurations grows exponentially. Within this increasing complexity, developers, maintainers, and users cannot keep…
Scout: An Experienced Guide to Find the Best Cloud Configuration
Chin-Jung Hsu, Vivek Nair, Tim Menzies +1
Finding the right cloud configuration for workloads is an essential step to ensure good performance and contain running costs. A poor choice of cloud configuration decreases applic…
Data-Driven Search-based Software Engineering
Vivek Nair, Amritanshu Agrawal, Jianfeng Chen +6
This paper introduces Data-Driven Search-based Software Engineering (DSE), which combines insights from Mining Software Repositories (MSR) and Search-based Software Engineering (SB…
Finding Faster Configurations using FLASH
Vivek Nair, Zhe Yu, Tim Menzies +2
Finding good configurations for a software system is often challenging since the number of configuration options can be large. Software engineers often make poor choices about conf…
Low-Level Augmented Bayesian Optimization for Finding the Best Cloud VM
Chin-Jung Hsu, Vivek Nair, Vincent W. Freeh +1
With the advent of big data applications, which tends to have longer execution time, choosing the right cloud VM to run these applications has significant performance as well as ec…
FLASH: A Faster Optimizer for SBSE Tasks
Vivek Nair, Zhe Yu, Tim Menzies
Most problems in search-based software engineering involve balancing conflicting objectives. Prior approaches to this task have required a large number of evaluations- making them…
Using Bad Learners to find Good Configurations
Vivek Nair, Tim Menzies, Norbert Siegmund +1
Finding the optimally performing configuration of a software system for a given setting is often challenging. Recent approaches address this challenge by learning performance model…
Faster Discovery of Faster System Configurations with Spectral Learning
Vivek Nair, Tim Menzies, Norbert Siegmund +1
Despite the huge spread and economical importance of configurable software systems, there is unsatisfactory support in utilizing the full potential of these systems with respect to…
Beyond Evolutionary Algorithms for Search-based Software Engineering
Jianfeng Chen, Vivek Nair, Tim Menzies
Context: Evolutionary algorithms typically require a large number of evaluations (of solutions) to converge - which can be very slow and expensive to evaluate.Objective: To solve s…
Why is Differential Evolution Better than Grid Search for Tuning Defect Predictors?
Wei Fu, Vivek Nair, Tim Menzies
Context: One of the black arts of data mining is learning the magic parameters which control the learners. In software analytics, at least for defect prediction, several methods, l…
"Sampling"' as a Baseline Optimizer for Search-based Software Engineering
Jianfeng Chen, Vivek Nair, Rahul Krishna +1
Increasingly, Software Engineering (SE) researchers use search-based optimization techniques to solve SE problems with multiple conflicting objectives. These techniques often apply…