arXiv subjects

Alex Berke

Publications and source records attributed to Alex Berke.

14 recordsLinked to original sources

arXiv2026

Uncovering Relationships between Android Developers, User Privacy, and Developer Willingness to Reduce Fingerprinting Risks

The major mobile platforms, Android and iOS, have introduced changes that restrict user tracking to improve user privacy, yet apps continue to covertly track users via device fingerprinting. We study the opportunity to improve this dynamic with a case study on mobile fingerprinting that evaluates developers' perceptions of how well platforms protect user privacy and how developers perceive platform privacy interventions. Specifically, we study developers' willingness to make changes to protect users from fingerprinting and how developers consider trade-offs between user privacy and developer effort. We do this via a survey of 246 Android developers, presented with a hypothetical Android change that protects users from fingerprinting at the cost of additional developer effort. We find developers overwhelmingly (89%) support this change, even when they anticipate significant effort, yet prefer the change be optional versus required. Surprisingly, developers who use fingerprinting are six times more likely to support the change, despite being most impacted by it. We also find developers are most concerned about compliance and enforcement. In addition, our results show that while most rank iOS above Android for protecting user privacy, this distinction significantly reduces among developers very familiar with fingerprinting. Thus there is an important opportunity for platforms and developers to collaboratively build privacy protections, and we present actionable ways platforms can facilitate this.

cs.CR↗

arXiv2025

Measuring risks inherent to our digital economies using Amazon purchase histories from US consumers

What do pickles and trampolines have in common? In this paper we show that while purchases for these products may seem innocuous, they risk revealing clues about customers' personal attributes - in this case, their race. As online retail and digital purchases become increasingly common, consumer data has become increasingly valuable, raising the risks of privacy violations and online discrimination. This work provides the first open analysis measuring these risks, using purchase histories crowdsourced from (N=4248) US Amazon.com customers and survey data on their personal attributes. With this limited sample and simple models, we demonstrate how easily consumers' personal attributes, such as health and lifestyle information, gender, age, and race, can be inferred from purchases. For example, our models achieve AUC values over 0.9 for predicting gender and over 0.8 for predicting diabetes status. To better understand the risks that highly resourced firms like Amazon, data brokers, and advertisers present to consumers, we measure how our models' predictive power scales with more data. Finally, we measure and highlight how different product categories contribute to inference risk in order to make our findings more interpretable and actionable for future researchers and privacy advocates.

cs.CY↗

arXiv2025

Evaluating Amazon Effects and the Limited Impact of COVID-19 With Purchases Crowdsourced from US Consumers

We leverage a recently published dataset of Amazon purchase histories, crowdsourced from thousands of US consumers, to study how online purchasing behaviors have changed over time, how changes vary across demographic groups, the impact of the COVID-19 pandemic, and relationships between online and offline retail. This work provides a case study in how consumer-level purchases data can reveal purchasing behaviors and trends beyond those available from aggregate metrics. For example, in addition to analyzing spending behavior, we develop new metrics to quantify changes in consumers' online purchase frequency and the diversity of products purchased, to better reflect the growing ubiquity and dominance of online retail. Between 2018 and 2022 these consumer-level metrics grew on average by more than 85%, peaking in 2021. We find a steady upward trend in individuals' online purchasing prior to COVID-19, with a significant increase in the first year of COVID, but without a lasting effect. Purchasing behaviors in 2022 were no greater than the result of the pre-pandemic trend. We also find changes in purchasing significantly differ by demographics, with different responses to the pandemic. We further use the consumer-level data to show substitution effects between online and offline retail in sectors where Amazon heavily invested: books, shoes, and grocery. Prior to COVID we find year-to-year changes in the number of consumers making online purchases for books and shoes negatively correlated with changes in employment at local bookstores and shoe stores. During COVID we find online grocery purchasing negatively correlated with in-store grocery visits. This work demonstrates how crowdsourced, open purchases data can enable economic insights that may otherwise only be available to private firms.

cs.CY↗

arXiv2024

How Unique is Whose Web Browser? The role of demographics in browser fingerprinting among US users

Browser fingerprinting can be used to identify and track users across the Web, even without cookies, by collecting attributes from users' devices to create unique "fingerprints". This technique and resulting privacy risks have been studied for over a decade. Yet further research is limited because prior studies used data not publicly available. Additionally, data in prior studies lacked user demographics. Here we provide a first-of-its-kind dataset to enable further research. It includes browser attributes with users' demographics and survey responses, collected with informed consent from 8,400 US study participants. We use this dataset to demonstrate how fingerprinting risks differ across demographic groups. For example, we find lower income users are more at risk, and find that as users' age increases, they are both more likely to be concerned about fingerprinting and at real risk of fingerprinting. Furthermore, we demonstrate an overlooked risk: user demographics, such as gender, age, income level and race, can be inferred from browser attributes commonly used for fingerprinting, and we identify which browser attributes most contribute to this risk. Our data collection process also conducted an experiment to study what impacts users' likelihood to share browser data for open research, in order to inform future data collection efforts, with responses from 12,461 total participants. Female participants were significantly less likely to share their browser data, as were participants who were shown the browser data we asked to collect. Overall, we show the important role of user demographics in the ongoing work that intends to assess fingerprinting risks and improve user privacy, with findings to inform future privacy enhancing browser developments. The dataset and data collection tool we provide can be used to further study research questions not addressed in this work.

cs.CY↗

arXiv2024

Insights from an experiment crowdsourcing data from thousands of US Amazon users: The importance of transparency, money, and data use

Data generated by users on digital platforms are a crucial resource for advocates and researchers interested in uncovering digital inequities, auditing algorithms, and understanding human behavior. Yet data access is often restricted. How can researchers both effectively and ethically collect user data? This paper shares an innovative approach to crowdsourcing user data to collect otherwise inaccessible Amazon purchase histories, spanning 5 years, from more than 5000 US users. We developed a data collection tool that prioritizes participant consent and includes an experimental study design. The design allows us to study multiple aspects of privacy perception and data sharing behavior. Experiment results (N=6325) reveal both monetary incentives and transparency can significantly increase data sharing. Age, race, education, and gender also played a role, where female and less-educated participants were more likely to share. Our study design enables a unique empirical evaluation of the "privacy paradox", where users claim to value their privacy more than they do in practice. We set up both real and hypothetical data sharing scenarios and find measurable similarities and differences in share rates across these contexts. For example, increasing monetary incentives had a 6 times higher impact on share rates in real scenarios. In addition, we study participants' opinions on how data should be used by various third parties, again finding demographics have a significant impact. Notably, the majority of participants disapproved of government agencies using purchase data yet the majority approved of use by researchers. Overall, our findings highlight the critical role that transparency, incentive design, and user demographics play in ethical data collection practices, and provide guidance for future researchers seeking to crowdsource user generated data.

cs.CY↗

arXiv2023

zkTax: A pragmatic way to support zero-knowledge tax disclosures

Tax returns contain key financial information of interest to third parties: public officials are asked to share financial data for transparency, companies seek to assess the financial status of business partners, and individuals need to prove their income to landlords or to receive benefits. Tax returns also contain sensitive data such that sharing them in their entirety undermines privacy. We introduce a zero-knowledge tax disclosure system (zkTax) that allows individuals and organizations to make provable claims about select information in their tax returns without revealing additional information, which can be independently verified by third parties. The system consists of three distinct services that can be distributed: a tax authority provides tax documents signed with a public key; a Redact & Prove Service enables users to produce a redacted version of the tax documents with a zero-knowledge proof attesting the provenance of the redacted data; a Verify Service enables anyone to verify the proof. We implement a prototype with a user interface, compatible with U.S. tax forms, and demonstrate how this design could be implemented with minimal changes to existing tax infrastructure. Our system is designed to be extensible to other contexts and jurisdictions. This work provides a practical example of how distributed tools leveraging cryptography can enhance existing government or financial infrastructures, providing immediate transparency alongside privacy without system overhauls.

cs.CR↗

arXiv2022

Routing with Privacy for Drone Package Delivery Systems

Unmanned aerial vehicles (UAVs), or drones, are increasingly being used to deliver goods from vendors to customers. To safely conduct these operations at scale, drones are required to broadcast position information as codified in remote identification (remote ID) regulations. However, location broadcast of package delivery drones introduces a privacy risk for customers using these delivery services: Third-party observers may leverage broadcast drone trajectories to link customers with their purchases, potentially resulting in a wide range of privacy risks. We propose a probabilistic definition of privacy risk based on the likelihood of associating a customer to a vendor given a package delivery route. Next, we quantify these risks, enabling drone operators to assess privacy risks when planning delivery routes. We then evaluate the impacts of various factors (e.g., drone capacity) on privacy and consider the trade-offs between privacy and delivery wait times. Finally, we propose heuristics for generating routes with privacy guarantees to avoid exhaustive enumeration of all possible routes and evaluate their performance on several realistic delivery scenarios.

cs.CR↗

arXiv2022

Privacy Limitations Of Interest-based Advertising On The Web: A Post-mortem Empirical Analysis Of Google's FLoC

In 2020, Google announced it would disable third-party cookies in the Chrome browser to improve user privacy. In order to continue to enable interest-based advertising while mitigating risks of individualized user tracking, Google proposed FLoC. The FLoC algorithm assigns users to "cohorts" that represent groups of users with similar browsing behaviors so that ads can be served to users based on their cohort. In 2022, after testing FLoC in a real world trial, Google canceled the proposal with little explanation. In this work, we provide a post-mortem analysis of two critical privacy risks for FloC by applying an implementation of FLoC to a browsing dataset collected from over 90,000 U.S. devices over a one year period. First, we show how, contrary to its privacy goals, FLoC would have enabled cross-site user tracking by providing a unique identifier for users available across sites, similar to the third-party cookies FLoC was meant to be an improvement over. We show how FLoC cohort ID sequences observed over time can provide this identifier to trackers, even with third-party cookies disabled. We estimate the number of users in our dataset that could be uniquely identified by FLoC IDs is more than 50% after 3 weeks and more than 95% after 4 weeks. We also show how these risks increase when cohort data are combined with browser fingerprinting, and how our results underestimate the true risks FLoC would have posed in a real-world deployment. Second, we examine the risk of FLoC leaking sensitive demographic information. Although we find statistically significant differences in browsing behaviors between demographic groups, we do not find that FLoC significantly risks exposing race or income information about users in our dataset. Our contributions provide insights and example analyses for future approaches that seek to protect user privacy while monetizing the web.

cs.CY↗

arXiv2022

Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy

Location data collected from mobile devices represent mobility behaviors at individual and societal levels. These data have important applications ranging from transportation planning to epidemic modeling. However, issues must be overcome to best serve these use cases: The data often represent a limited sample of the population and use of the data jeopardizes privacy. To address these issues, we present and evaluate a system for generating synthetic mobility data using a deep recurrent neural network (RNN) which is trained on real location data. The system takes a population distribution as input and generates mobility traces for a corresponding synthetic population. Related generative approaches have not solved the challenges of capturing both the patterns and variability in individuals' mobility behaviors over longer time periods, while also balancing the generation of realistic data with privacy. Our system leverages RNNs' ability to generate complex and novel sequences while retaining patterns from training data. Also, the model introduces randomness used to calibrate the variation between the synthetic and real data at the individual level. This is to both capture variability in human mobility, and protect user privacy. Location based services (LBS) data from more than 22,700 mobile devices were used in an experimental evaluation across utility and privacy metrics. We show the generated mobility data retain the characteristics of the real data, while varying from the real data at the individual level, and where this amount of variation matches the variation within the real data.

cs.LG↗

arXiv2021

Private Delivery Networks -- Extended Abstract

The past decade has seen tremendous shifts in how people live, work, and buy goods, with an increased reliance on e-commerce and deliveries. Purchase histories generated through e-commerce can be highly personal, revealing identifying information about individuals and households. Constructing profiles from these data allows for the targeting of individuals and communities through practices such as targeted marketing and information campaigns. Furthermore, when purchase profiles are connected with delivery addresses, these data can measure the demographics of a local community and allow for individualized targeting to reach beyond the digital realm to the physical one. Events that accelerated shifts towards e-commerce, such as an infectious disease epidemic, have also widened equity gaps. This work is about alternative e-commerce delivery network models that address both rising privacy and wealth inequality concerns. This includes strategies that mask and add noise to purchase histories, and allow people to "buy privacy" through charitable contributions.

cs.CY↗

arXiv2020

Urban Mobility Swarms: A Scalable Implementation

We present a system to coordinate 'urban mobility swarms' in order to promote the use and safety of lightweight, sustainable transit, while enhancing the vibrancy and community fabric of cities. This work draws from behavior exhibited by swarms of nocturnal insects, such as crickets and fireflies, whereby synchrony unifies individuals in a decentralized network. Coordination naturally emerges in these cases and provides a compelling demonstration of 'strength in numbers'. Our work is applied to coordinating lightweight vehicles, such as bicycles, which are automatically inducted into ad-hoc 'swarms', united by the synchronous pulsation of light. We model individual riders as nodes in a decentralized network and synchronize their behavior via a peer-to-peer message protocol and algorithm, which preserves individual privacy. Nodes broadcast over radio with a transmission range tuned to localize swarm membership. Nodes then join or disconnect from others based on proximity, accommodating the dynamically changing topology of urban mobility networks. This paper provides a technical description of our system, including the protocol and algorithm to coordinate the swarming behavior that emerges from it. We also demonstrate its implementation in code, circuity, and hardware, with a system prototype tested on a city bike-share. In doing so, we evince the scalability of our system. Our prototype uses low-cost components, and bike-share programs, which manage bicycle fleets distributed across cities, could deploy the system at city-scale. Our flexible, decentralized design allows additional bikes to then connect with the network, enhancing its scale and impact.

cs.MA↗

arXiv2020

Assessing Disease Exposure Risk with Location Data: A Proposal for Cryptographic Preservation of Privacy

Governments and researchers around the world are implementing digital contact tracing solutions to stem the spread of infectious disease, namely COVID-19. Many of these solutions threaten individual rights and privacy. Our goal is to break past the false dichotomy of effective versus privacy-preserving contact tracing. We offer an alternative approach to assess and communicate users' risk of exposure to an infectious disease while preserving individual privacy. Our proposal uses recent GPS location histories, which are transformed and encrypted, and a private set intersection protocol to interface with a semi-trusted authority. There have been other recent proposals for privacy-preserving contact tracing, based on Bluetooth and decentralization, that could further eliminate the need for trust in authority. However, solutions with Bluetooth are currently limited to certain devices and contexts while decentralization adds complexity. The goal of this work is two-fold: we aim to propose a location-based system that is more privacy-preserving than what is currently being adopted by governments around the world, and that is also practical to implement with the immediacy needed to stem a viral outbreak.

cs.CR↗

arXiv2020

Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic

Containment, the key strategy in quickly halting an epidemic, requires rapid identification and quarantine of the infected individuals, determination of whom they have had close contact with in the previous days and weeks, and decontamination of locations the infected individual has visited. Achieving containment demands accurate and timely collection of the infected individual's location and contact history. Traditionally, this process is labor intensive, susceptible to memory errors, and fraught with privacy concerns. With the recent almost ubiquitous availability of smart phones, many people carry a tool which can be utilized to quickly identify an infected individual's contacts during an epidemic, such as the current 2019 novel Coronavirus crisis. Unfortunately, the very same first-generation contact tracing tools have been used to expand mass surveillance, limit individual freedoms and expose the most private details about individuals. We seek to outline the different technological approaches to mobile-phone based contact-tracing to date and elaborate on the opportunities and the risks that these technologies pose to individuals and societies. We describe advanced security enhancing approaches that can mitigate these risks and describe trade-offs one must make when developing and deploying any mass contact-tracing technology. With this paper, our aim is to continue to grow the conversation regarding contact-tracing for epidemic and pandemic containment and discuss opportunities to advance this space. We invite feedback and discussion.

cs.CR↗

arXiv2019

The tradeoff between the utility and risk of location data and implications for public good

High-resolution individual geolocation data passively collected from mobile phones is increasingly sold in private markets and shared with researchers. This data poses significant security, privacy, and ethical risks: it's been shown that users can be re-identified in such datasets, and its collection rarely involves their full consent or knowledge. This data is valuable to private firms (e.g. targeted marketing) but also presents clear value as a public good. Recent public interest research has demonstrated that high-resolution location data can more accurately measure segregation in cities and provide inexpensive transit modeling. But as data is aggregated to mitigate its re-identifiability risk, its value as a good diminishes. How do we rectify the clear security and safety risks of this data, its high market value, and its potential as a resource for public good? We extend the recently proposed concept of a tradeoff curve that illustrates the relationship between dataset utility and privacy. We then hypothesize how this tradeoff differs between private market use and its potential use for public good. We further provide real-world examples of how high resolution location data, aggregated to varying degrees of privacy protection, can be used in the public sphere and how it is currently used by private firms.

cs.CY↗