Zobrazeno 1 - 10
of 323
pro vyhledávání: '"FEAMSTER, NICK"'
Model checkers and consistency checkers detect critical errors in router configurations, but these tools require significant manual effort to develop and maintain. LLM-based Q&A models have emerged as a promising alternative, allowing users to query
Externí odkaz:
http://arxiv.org/abs/2411.14283
Autor:
Sarwar, Zain, Tran, Van, Bhagoji, Arjun Nitin, Feamster, Nick, Zhao, Ben Y., Chakraborty, Supriyo
Machine learning (ML) models often require large amounts of data to perform well. When the available data is limited, model trainers may need to acquire more data from external sources. Often, useful data is held by private entities who are hesitant
Externí odkaz:
http://arxiv.org/abs/2410.08432
Low Latency, Low Loss, and Scalable Throughput (L4S), as an emerging router-queue management technique, has seen steady deployment in the industry. An L4S-enabled router assigns each packet to the queue based on the packet header marking. Currently,
Externí odkaz:
http://arxiv.org/abs/2410.06112
Autor:
Tran, Van Hong, Mehrotra, Aarushi, Sharma, Ranya, Chetty, Marshini, Feamster, Nick, Frankenreiter, Jens, Strahilevitz, Lior
To protect consumer privacy, the California Consumer Privacy Act (CCPA) mandates that businesses provide consumers with a straightforward way to opt out of the sale and sharing of their personal information. However, the control that businesses enjoy
Externí odkaz:
http://arxiv.org/abs/2409.09222
Autor:
Chu, Andrew, Jiang, Xi, Liu, Shinan, Bhagoji, Arjun, Bronzino, Francesco, Schmitt, Paul, Feamster, Nick
Many problems in computer networking rely on parsing collections of network traces (e.g., traffic prioritization, intrusion detection). Unfortunately, the availability and utility of these collections is limited due to privacy concerns, data stalenes
Externí odkaz:
http://arxiv.org/abs/2406.02784
Despite significant investments in access network infrastructure, universal access to high-quality Internet connectivity remains a challenge. Policymakers often rely on large-scale, crowdsourced measurement datasets to assess the distribution of acce
Externí odkaz:
http://arxiv.org/abs/2405.11138
Autor:
Schaffner, Brennan, Bhagoji, Arjun Nitin, Cheng, Siyuan, Mei, Jacqueline, Shen, Jay L., Wang, Grace, Chetty, Marshini, Feamster, Nick, Lakier, Genevieve, Tan, Chenhao
Moderating user-generated content on online platforms is crucial for balancing user safety and freedom of speech. Particularly in the United States, platforms are not subject to legal constraints prescribing permissible content. Each platform has thu
Externí odkaz:
http://arxiv.org/abs/2405.05225
In 2021, the Broadband Equity, Access, and Deployment (BEAD) program allocated $42.45 billion to enhance high-speed internet access across the United States. As part of this funding initiative, The Federal Communications Commission (FCC) developed a
Externí odkaz:
http://arxiv.org/abs/2404.04189
Autor:
Tran, Van, Mehrotra, Aarushi, Chetty, Marshini, Feamster, Nick, Frankenreiter, Jens, Strahilevitz, Lior
The widespread sharing of consumers personal information with third parties raises significant privacy concerns. The California Consumer Privacy Act (CCPA) mandates that online businesses offer consumers the option to opt out of the sale and sharing
Externí odkaz:
http://arxiv.org/abs/2403.17225
Machine learning has shown tremendous potential for improving the capabilities of network traffic analysis applications, often outperforming simpler rule-based heuristics. However, ML-based solutions remain difficult to deploy in practice. Many exist
Externí odkaz:
http://arxiv.org/abs/2402.06099