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new conference paper: Auditing for Racial Discrimination in the Delivery of Education Ads

Our new paper “Auditing for Racial Discrimination in the Delivery of Education Ads” will appear at the ACM FAccT Conference in Rio de Janeiro in June 2024.

From the abstract:

Experiments showing educational ads for for-profit schools are disproportionately shown to Blacks at statistically significant levels. (from [Imana24a], figure 4).
Experiments showing educational ads for for-profit schools are disproportionately shown to Blacks at statistically significant levels. (from [Imana24a], figure 4).

Digital ads on social-media platforms play an important role in shaping access to economic opportunities. Our work proposes and implements a new third-party auditing method that can evaluate racial bias in the delivery of ads for education opportunities. Third-party auditing is important because it allows external parties to demonstrate presence or absence of bias in social-media algorithms. Education is a domain with legal protections against discrimination and concerns of racial-targeting, but bias induced by ad delivery algorithms has not been previously explored in this domain. Prior audits demonstrated discrimination in platforms’ delivery of ads to users for housing and employment ads. These audit findings supported legal action that prompted Meta to change their ad-delivery algorithms to reduce bias, but only in the domains of housing, employment, and credit. In this work, we propose a new methodology that allows us to measure racial discrimination in a platform’s ad delivery algorithms for education ads. We apply our method to Meta using ads for real schools and observe the results of delivery. We find evidence of racial discrimination in Meta’s algorithmic delivery of ads for education opportunities, posing legal and ethical concerns. Our results extend evidence of algorithmic discrimination to the education domain, showing that current bias mitigation mechanisms are narrow in scope, and suggesting a broader role for third-party auditing of social media in areas where ensuring non-discrimination is important.

This work was reported on in an article by Sam Biddle in the Intercept, by Thomas Claburn at The Register, and in ACM Tech News.

This paper is a joint work of Basileal Imana and Aleksandra Korolova from Princeton University, and John Heidemann from USC/ISI. We thank the NSF for supporting this work (CNS-1956435, CNS-
1916153, CNS-2333448, CNS-1943584, CNS-2344925, CNS-2319409,
and CNS-1925737).

Data from this paper is available from our website.

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new journal paper: “Deep Dive into NTP Pool’s Popularity and Mapping”

Our new paper “Deep Dive into NTP Pool’s Popularity and Mapping” will appear in the SIGMETRICS 2024 conference and concurrently in the ACM Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 8, no. 1, March 2024.

From the abstract:

Number of ASes that are time providers per country (Figure 8 from [Moura24a]).

Time synchronization is of paramount importance on the Internet, with the Network Time Protocol (NTP) serving as the primary synchronization protocol. The NTP Pool, a volunteer-driven initiative launched two decades ago, facilitates connections between clients and NTP servers. Our analysis of root DNS queries reveals that the NTP Pool has consistently been the most popular time service. We further investigate the DNS component (GeoDNS) of the NTP Pool, which is responsible for mapping clients to servers. Our findings indicate that the current algorithm is heavily skewed, leading to the emergence of time monopolies for entire countries. For instance, clients in the US are served by 551 NTP servers, while clients in Cameroon and Nigeria are served by only one and two servers, respectively, out of the 4k+ servers available in the NTP Pool. We examine the underlying assumption behind GeoDNS for these mappings and discover that time servers located far away can still provide accurate clock time information to clients. We have shared our findings with the NTP Pool operators, who acknowledge them and plan to revise their algorithm to enhance security.

This paper is a joint work of

Giovane C. M. Moura1,2, Marco Davids1, Caspar Schutijser1, Christian Hesselman1,3, John Heidemann4,5, and Georgios Smaragdakis2 with 1: SIDN Labs, 2 Technical University, Delft, 3: the University of Twente, 4: the University of Southern California/Information Sciences Institute, 5: USC/Computer Science Dept. This work was supported by the RIPE NCC (via Atlas), the Root Operators and DNS-OARC (for DITL), SIDN Labs time.nl project, the Twente University Centre for Cyber Security Resarch, NSF projects CNS-2212480, CNS-2319409, the European Research Council ResolutioNet (679158), Duth 6G Future Network Services project, the EU programme Horizon Europe grants SEPTON (101094901), MLSysOps (101092912), and TANGO (101070052).

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New conference paper: Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest

Our new paper “Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest” will appear at The 26th ACM Conference On Computer-Supported Cooperative Work And Social Computing (CSCW 2023).

From the abstract:

Overview of our proposed platform-supported framework for auditing relevance estimators while protecting the privacy of audit participants and the business interests of platforms.

Concerns of potential harmful outcomes have prompted proposal of legislation in both the U.S. and the E.U. to mandate a new form of auditing where vetted external researchers get privileged access to social media platforms. Unfortunately, to date there have been no concrete technical proposals to provide such auditing, because auditing at scale risks disclosure of users’ private data and platforms’ proprietary algorithms. We propose a new method for platform-supported auditing that can meet the goals of the proposed legislation. The first contribution of our work is to enumerate the challenges and the limitations of existing auditing methods to implement these policies at scale. Second, we suggest that limited, privileged access to relevance estimators is the key to enabling generalizable platform-supported auditing of social media platforms by external researchers. Third, we show platform-supported auditing need not risk user privacy nor disclosure of platforms’ business interests by proposing an auditing framework that protects against these risks. For a particular fairness metric, we show that ensuring privacy imposes only a small constant factor increase (6.34x as an upper bound, and 4x for typical parameters) in the number of samples required for accurate auditing. Our technical contributions, combined with ongoing legal and policy efforts, can enable public oversight into how social media platforms affect individuals and society by moving past the privacy-vs-transparency hurdle.

A 2-minute video overview of the work can be found here.

This paper is a joint work of Basileal Imana from USC, Aleksandra Korolova from Princeton University, and John Heidemann from USC/ISI.

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new workshop report “Overcoming Measurement Barriers to Internet Research” (WOMBIR 2021) in ACM CCR

WOMBIR 2021 was the NSF-sponsored Workshop on Overcoming Measurement Barriers to Internet Research. This workshop was hold in two sessions over several days in January and April 2021, chaired by k.c. claffy, David Clark, Fabian Bustamente, John Heidemann, and Mattijs Monjker. The final report includes contributions from Aaron Schulman and Ellen Zegura as well as all the workshop participants.

From the abstract:

In January and April 2021 we held the Workshop on Overcoming Measurement Barriers to Internet Research (WOMBIR) with the goal of understanding challenges in network and security data set collection and sharing. Most workshop attendees provided white papers describing their perspectives, and many participated in short-talks and discussion in two virtual workshops over five days. That discussion produced consensus around several points. First, many aspects of the Internet are characterized by decreasing visibility of important network properties, which is in tension with the Internet’s role as critical infrastructure. We discussed three specific research areas that illustrate this tension: security, Internet access; and mobile networking. We discussed visibility challenges at all layers of the networking stack, and the challenge of gathering data and validating inferences. Important data sets require longitudinal (long-term, ongoing) data collection and sharing, support for which is more challenging for Internet research than other fields. We discussed why a combination of technical and policy methods are necessary to safeguard privacy when using or sharing measurement data. Workshop participant proposed several opportunities to accelerate progress, some of which require coordination across government, industry, and academia.

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new journal paper “Detecting IoT Devices in the Internet” in IEEE/ACM Transactions on Networking

We have published a new journal paper “Detecting IoT Devices in the Internet” in IEEE/ACM Transactions on Networking, available at https://www.isi.edu/~johnh/PAPERS/Guo20c.pdf

Figure 5 from [Guo20c] showing per-device-type AS penetrations from 2013 to 2018 for 16 of the 23 device types we studies (omitting 7 device types appearing in less than10 ASes)

From the abstract of our journal paper:

Distributed Denial-of-Service (DDoS) attacks launched from compromised Internet-of-Things (IoT) devices have shown how vulnerable the Internet is to largescale DDoS attacks. To understand the risks of these attacks requires learning about these IoT devices: where are they? how many are there? how are they changing? This paper describes three new methods to find IoT devices on the Internet: server IP addresses in traffic, server names in DNS queries, and manufacturer information in TLS certificates. Our primary methods (IP addresses and DNS names) use knowledge of servers run by the manufacturers of these devices. Our third method uses TLS certificates obtained by active scanning. We have applied our algorithms to a number of observations. With our IP-based algorithm, we report detections from a university campus over 4 months and from traffic transiting an IXP over 10 days. We apply our DNS-based algorithm to traffic from 8 root DNS servers from 2013 to 2018 to study AS-level IoT deployment. We find substantial growth (about 3.5×) in AS penetration for 23 types of IoT devices and modest increase in device type density for ASes detected with these device types (at most 2 device types in 80% of these ASes in 2018). DNS also shows substantial growth in IoT deployment in residential households from 2013 to 2017. Our certificate-based algorithm finds 254k IP cameras and network video recorders from 199 countries around the world.

We make operational traffic we captured from 10 IoT devices we own public at https://ant.isi.edu/datasets/iot/. We also use operational traffic of 21 IoT devices shared by University of New South Wales at http://149.171.189.1/.

This journal paper is joint work of Hang Guo and  John Heidemann from USC/ISI.