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congratulations to ASM Rizvi for his PhD

I would like to congratulate Dr. ASM Rizvi for defending his PhD at the University of Southern California in June 2024 and completing his doctoral dissertation “Mitigating Attacks that Disrupt Online Services Without Changing Existing Protocols”.

From the dissertation abstract:

ASM Rizvi and John Heidemann, after Rizvi's PhD defense.

Service disruption is undesirable in today’s Internet connectivity due to its impacts on enterprise profits, reputation, and user satisfaction. We describe service disruption as any targeted interruptions caused by malicious parties in the regular user-to-service interactions and functionalities that affect service performance and user experience. In this thesis, we propose new methods that tackle service disruptive attacks using measurement without changing existing Internet protocols. Although our methods do not guarantee defense against all the attack types, our example defense systems prove that our methods generally work to handle diverse attacks. To validate our thesis, we demonstrate defense systems against three disruptive attack types. First, we mitigate Distributed Denial-of-Service (DDoS) attacks that target an online service. Second, we handle brute-force password attacks that target the users of a service. Third, we detect malicious routing detours to secure the path from the users to the server. We provide the first public description of DDoS defenses based on anycast and filtering for the network operators. Then, we show the first moving target defense utilizing IPv6 to defeat password attacks. We also demonstrate how regular observation of latency helps cellular users, carriers, and national agencies to find malicious routing detours. As a supplemental outcome, we show the effectiveness of measurements in finding performance issues and ways to improve using existing protocols. These examples show that our idea applies to different network parts, even if we may not mitigate all the attack types.

Rizvi’s PhD work was supported by the U.S. Department of Homeland Security’s HSARPA Cyber Security Division (HSHQDC-17-R-B0004-TTA.02-0006-I, PAADDOS) in a joint project with the Netherlands Organisation for scientific research (4019020199), the U.S. National Science Foundation (grant NSF OAC-1739034, DDIDD; CNS-2319409, PIMAWAT; CRI-8115780, CLASSNET; CNS-1925737, DIINER ) and U.S. DARPA (HR001120C0157, SABRES), and Akamai.

Most data from his papers is available at no cost from ANT; please see specific publications for details.

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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.