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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 conference paper “Efficient Processing of Streaming Data using Multiple Abstractions” at IEEE Cloud

We have published a new paper “Efficient Processing of Streaming Data using Multiple Abstractions” at the IEEE Cloud 2021 conference. (to be available at https://conferences.computer.org/cloud/2021/)

We show that one framework can efficiently support multiple abstractions. We provide three abstractions of Block, Windowed, and Stateful streaming and demonstrate that many application classes can be developed with ease, correctness, and low processing latency.

From the abstract of our paper:

Large websites and distributed systems employ sophisticated analytics to evaluate successes to celebrate and problems to be addressed. As analytics grow, different teams often require different frameworks, with dozens of packages supporting with streaming and batch processing, SQL and no-SQL. Bringing multiple frameworks to bear on a large, changing dataset often create challenges where data transitions—these impedance mismatches can create brittle glue logic and performance problems that consume developer time. We propose Plumb, a meta-framework that can bridge three different abstractions to meet the needs of a large class of applications in a common workflow. Large-block streaming (Block-Streaming) is suitable for single-pass applications that care about the temporal and spatial locality. Windowed-Streaming allows applications to process a group of data and many reductions. Stateful-Streaming enables applications to keep a long-term state and always-on behavior. We show that it is possible to bridge abstractions, with a common, high-level workflow specification, while the system transitions data batch processing and block- and record-level streaming as required. The challenge in bridging abstractions is to minimize latency while allowing applications to select between sequential and parallel operation, while handling out-of-order data delivery, component failures, and providing clear semantics in the face of missing data. We demonstrate these abstractions evaluating a 10-stage workflow of DNS analytics that has been in production use with Plumb for 2 years, comparing to a brittle hand-built system that has run for more than 3 years.

This conference paper is joint work of Abdul Qadeer and  John Heidemann from USC/ISI.

Plumb is open source software and will be available at: https://ant.isi.edu/software/plumb/index.html

Update 2021-09-26: This paper was given a “special paper award” at IEEE Conference on Cloud Computing 2021! Congratulations, Abdul!

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New paper “Bidirectional Anycast/Unicast Probing (BAUP): Optimizing CDN Anycast” at IFIP TMA 2020

We published a new paper “Bidirectional Anycast/Unicast Probing (BAUP): Optimizing CDN Anycast” by Lan Wei (University of Southern California/ ISI), Marcel Flores (Verizon Digital Media Services), Harkeerat Bedi (Verizon Digital Media Services), John Heidemann (University of Southern California/ ISI) at Network Traffic Measurement and Analysis Conference 2020.

From the abstract:

IP anycast is widely used today in Content Delivery Networks (CDNs) and for Domain Name System (DNS) to provide efficient service to clients from multiple physical points-of-presence (PoPs). Anycast depends on BGP routing to map users to PoPs, so anycast efficiency depends on both the CDN operator and the routing policies of other ISPs. Detecting and diagnosing
inefficiency is challenging in this distributed environment. We propose Bidirectional Anycast/Unicast Probing (BAUP), a new approach that detects anycast routing problems by comparing anycast and unicast latencies. BAUP measures latency to help us identify problems experienced by clients, triggering traceroutes to localize the cause and suggest opportunities for improvement. Evaluating BAUP on a large, commercial CDN, we show that problems happens to 1.59% of observers, and we find multiple opportunities to improve service. Prompted by our work, the CDN changed peering policy and was able to significantly reduce latency, cutting median latency in half (40 ms to 16 ms) for regions with more than 100k users.

The data from this paper is publicly available from RIPE Atlas, please see paper reference for measurement IDs.

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Papers

new paper “Improving Coverage of Internet Outage Detection in Sparse Blocks”

We will publish a new paper “Improving Coverage of Internet Outage Detection in Sparse Blocks” by Guillermo Baltra and John Heidemann in the Passive and Active Measurement Conference (PAM 2020) in Eugene, Oregon, USA, on March 30, 2020.

From the abstract:

There is a growing interest in carefully observing the reliability of the Internet’s edge. Outage information can inform our understanding of Internet reliability and planning, and it can help guide operations. Active outage detection methods provide results for more than 3M blocks, and passive methods more than 2M, but both are challenged by sparse blocks where few addresses respond or send traffic. We propose a new Full Block Scanning (FBS) algorithm to improve coverage for active scanning by providing reliable results for sparse blocks by gathering more information before making a decision. FBS identifies sparse blocks and takes additional time before making decisions about their outages, thereby addressing previous concerns about false outages while preserving strict limits on probe rates. We show that FBS can improve coverage by correcting 1.2M blocks that would otherwise be too sparse to correctly report, and potentially adding 1.7M additional blocks. FBS can be applied retroactively to existing datasets to improve prior coverage and accuracy.

This paper defines two algorithms: Full Block Scanning (FBS), to address false outages seen in active measurements of sparse blocks, and Lone Address Block Recovery (LABR), to handle blocks with one or two responsive addresses. We show that these algorithms increase coverage, from a nominal 67% (and as low as 53% after filtering) of responsive blocks before to 5.7M blocks, 96% of responsive blocks.
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Papers Publications

new paper “Identifying Important Internet Outages” at the Sixth National Symposium for NSF REU Research in Data Science, Systems, and Security

We will publish a new paper “Identifying Important Internet Outages” by Ryan Bogutz, Yuri Pradkin, and John Heidemann, in the Sixth National Symposium for NSF REU Research in Data Science, Systems, and Security in Los Angeles, California, USA, on December 12, 2019.

From the abstract:

[Bogutz19a, figure 1]: Our sideboard showing important outages on 2019-03-08, including this outage in Venezuela.

Today, outage detection systems can track outages across the whole IPv4 Internet—millions of networks. However, it becomes difficult to find meaningful, interesting events in this huge dataset, since three months of data can easily include 660M observations and thousands of outage events. We propose an outage reporting system that sifts through this data to find the most interesting events. We explore multiple metrics to evaluate interesting”, reflecting the size and severity of outages. We show that defining interest as the product of size by severity works well, avoiding degenerate cases like complete outages affecting a few people, and apparently large outages that affect only a small fraction of people in an area. We have integrated outage reporting into our existing public website (https://outage.ant.isi.edu) with the goal of making near-real-time outage information accessible to the general public. Such data can help answer questions like “what are the most significant outages today?”, did Florida have major problems in an ongoing hurricane?”, and
“are there power outages in Venezuela?”.

The data from this paper is available publicly and in our website. The technical report ISI-TR-735 includes some additional data.

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Papers Publications

new conference paper “Cache Me If You Can: Effects of DNS Time-to-Live” at ACM IMC 2019

We will publish a new paper “Cache Me If You Can: Effects of DNS Time-to-Live” by Giovane C. M. Moura, John Heidemann, Ricardo de O. Schmidt, and Wes Hardaker, in the ACM Internet Measurements Conference (IMC 2019) in Amsterdam, the Netherlands.

From the abstract:

Figure 10a from [Moura19b], showing the distribution of latency with small TTLs before (right in blue) and with larger TTLs after (left in red) the .uy domain reviewed our work and lengthened their domain’s cache lifetimes to reduce latency to their customers.

DNS depends on extensive caching for good performance, and every DNS zone owner must set Time-to-Live (TTL) values to control their DNS caching. Today there is relatively little guidance backed by research about how to set TTLs, and operators must balance conflicting demands of caching against agility of configuration. Exactly how TTL value choices affect operational networks is quite challenging to understand due to interactions across the distributed DNS service, where resolvers receive TTLs in different ways (answers and hints), TTLs are specified in multiple places (zones and their parent’s glue), and while DNS resolution must be security-aware. This paper provides the first careful evaluation of how these multiple, interacting factors affect the effective cache lifetimes of DNS records, and provides recommendations for how to configure DNS TTLs based on our findings. We provide recommendations in TTL choice for different situations, and for where they must be configured. We show that longer TTLs have significant promise in reducing latency, reducing it from 183ms to 28.7ms for one country-code TLD.

We have also reported on this work at the RIPE and APNIC blogs.

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Publications Technical Report

new technical report “Plumb: Efficient Processing of Multi-User Pipelines (Poster)”

We released a new technical report “Plumb: Efficient Processing of Multi-User Pipelines (Poster)”, by Abdul Qadeer and John Heidemann, as ISI-TR-731.  This work was originally presented at ACM Symposium on Cloud Computing (the poster abstract is available at ACM). The poster abstract with a small version of the poster is available at https://www.isi.edu/publications/trpublic/pdfs/isi-tr-731.pdf

aqadeer at SoCC 2018 Carlsbad CA

From the abstract:

As the field of big data analytics matures, workflows are increasingly complex and often include components that are shared by different users. Individual workflows often include multiple stages, and when groups build on each other’s work it is easy to lose track of computation that may be shared across different groups.

The contribution of this poster is to provide an organization-wide processing substrate Plumb that can be used to solve commonly occurring problems and to achieve a common goal. Plumb makes multi-user sharing a first-class concern by providing pipeline-graph abstraction. This abstraction is simple and based on fundamental model of input-processing-output but is powerful to capture processing and data duplication. Plumb then employs best available solutions to tackle problems of large-block processing under structural and computational skew without user intervention.

We expect to release the Plumb software this fall; please contact us if you have questions or interest in using it.

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new conference paper “Who Knocks at the IPv6 Door? Detecting IPv6 Scanning” at ACM IMC 2018

We have published a new paper “Who Knocks at the IPv6 Door? Detecting IPv6 Scanning” by Kensuke Fukuda and John Heidemann, in the ACM Internet Measurements Conference (IMC 2018) in Boston, Mass., USA.

DNS backscatter from IPv4 and IPv6 ([Fukuda18a], figure 1).
From the abstract:

DNS backscatter detects internet-wide activity by looking for common reverse DNS lookups at authoritative DNS servers that are high in the DNS hierarchy. Both DNS backscatter and monitoring unused address space (darknets or network telescopes) can detect scanning in IPv4, but with IPv6’s vastly larger address space, darknets become much less effective. This paper shows how to adapt DNS backscatter to IPv6. IPv6 requires new classification rules, but these reveal large network services, from cloud providers and CDNs to specific services such as NTP and mail. DNS backscatter also identifies router interfaces suggesting traceroute-based topology studies. We identify 16 scanners per week from DNS backscatter using observations from the B-root DNS server, with confirmation from backbone traffic observations or blacklists. After eliminating benign services, we classify another 95 originators in DNS backscatter as potential abuse. Our work also confirms that IPv6 appears to be less carefully monitored than IPv4.

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Papers Publications

new conference paper “LDplayer: DNS Experimentation at Scale” at ACM IMC 2018

We have published a new paper LDplayer: DNS Experimentation at Scale by Liang Zhu and John Heidemann, in the ACM Internet Measurements Conference (IMC 2018) in Boston, Mass., USA.

Figure 14a: Evaluation of server memory with different TCP timeouts and minimal RTT (<1 ms). Trace: B-Root-17a. Protocol: TLS

From the abstract:

DNS has evolved over the last 20 years, improving in security and privacy and broadening the kinds of applications it supports. However, this evolution has been slowed by the large installed base and the wide range of implementations. The impact of changes is difficult to model due to complex interactions between DNS optimizations, caching, and distributed operation. We suggest that experimentation at scale is needed to evaluate changes and facilitate DNS evolution. This paper presents LDplayer, a configurable, general-purpose DNS experimental framework that enables DNS experiments to scale in several dimensions: many zones, multiple levels of DNS hierarchy, high query rates, and diverse query sources. LDplayer provides high fidelity experiments while meeting these requirements through its distributed DNS query replay system, methods to rebuild the relevant DNS hierarchy from traces, and efficient emulation of this hierarchy on minimal hardware. We show that a single DNS server can correctly emulate multiple independent levels of the DNS hierarchy while providing correct responses as if they were independent. We validate that our system can replay a DNS root traffic with tiny error (± 8 ms quartiles in query timing and ± 0.1% difference in query rate). We show that our system can replay queries at 87k queries/s while using only one CPU, more than twice of a normal DNS Root traffic rate. LDplayer’s trace replay has the unique ability to evaluate important design questions with confidence that we capture the interplay of caching, timeouts, and resource constraints. As an example, we demonstrate the memory requirements of a DNS root server with all traffic running over TCP and TLS, and identify performance discontinuities in latency as a function of client RTT.

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new conference paper “The Policy Potential of Measuring Internet Outages” at TPRC

We have published a new paper “The Policy Potential of Measuring Internet Outages” in TPRC46, the Research Conference on Communications, Information and Internet Policy, to be presented on September 21, 2018 at the American University, Washington College of Law.

Outages from Hurricane Irma after landfall in Florida on 2017-09-11, observed with Trinocular.

From the abstract of our paper:

Today it is possible to evaluate the reliability of the Internet. Prior approaches to measure network reliability required telecommunications providers reporting the status of their own networks, resulting in limits on the precision, timeliness, and availability of the results. Recent work in Internet measurement has shown that network outages can be observed with active measurements from a few sites, and from passive measurements of network telescopes (large, unused address space) or large network services such as content-delivery networks. We suggest that these kinds of *third-party* observations of network outages can provide data that is precise and timely. We discuss early results of Trinocular, an outage detection system using active probing developed at the University of Southern California. Trinocular has been operating continuously since November 2013, and we provide (at no charge) data covering about 4 million network blocks from around the world. This paper describes some results of Trinocular showing outages in a large U.S. Internet Service Provider, and those resulting from the 2017 Hurricane Irma in Florida. Our data shows the impact of the Broadband America policy for always-on networks, and we discuss how it might be used to address future policy questions and assist in disaster planning and recovery.

Data we describe in this paper is at https://ant.isi.edu/datasets/outage/, with visualizations at https://ant.isi.edu/outage/world/.

This paper is joint work of John Heideman, Yuri Pradkin, and Guillermo Baltra from USC/ISI, with work carried out as part of LACANIC and DIVOICE projects with DHS S&T/CSD support.