198 citations · 479 across the 26 of their papers we have counts for
31 papers
Holding Agents Accountable with Verifiable Agent Insurance
Cheng Hua, Taro Tsuchiya, Nicolas Christin +1
While AI agents are useful for many tasks, they regularly misbehave in destructive ways, such as deleting important data. While prior work has focused on preventing such misbehavio…
Summary Statistic Privacy in Data Sharing
Zinan Lin, Shuaiqi Wang, Vyas Sekar +1
We study a setting where a data holder wishes to share data with a receiver, without revealing certain summary statistics of the data distribution (e.g., mean, standard deviation).…
Privately Customizing Prefinetuning to Better Match User Data in Federated Learning
Charlie Hou, Hongyuan Zhan, Akshat Shrivastava +4
In Federated Learning (FL), accessing private client data incurs communication and privacy costs. As a result, FL deployments commonly prefinetune pretrained foundation models on a…
Batching of Tasks by Users of Pseudonymous Forums: Anonymity Compromise and Protection
Alexander Goldberg, Giulia Fanti, Nihar B. Shah
There are a number of forums where people participate under pseudonyms. One example is peer review, where the identity of reviewers for any paper is confidential. When participatin…
On the Privacy Properties of GAN-generated Samples
Zinan Lin, Vyas Sekar, Giulia Fanti
The privacy implications of generative adversarial networks (GANs) are a topic of great interest, leading to several recent algorithms for training GANs with privacy guarantees. By…
Towards a Defense Against Federated Backdoor Attacks Under Continuous Training
Shuaiqi Wang, Jonathan Hayase, Giulia Fanti +1
Backdoor attacks are dangerous and difficult to prevent in federated learning (FL), where training data is sourced from untrusted clients over long periods of time. These difficult…
Strategic Latency Reduction in Blockchain Peer-to-Peer Networks
Weizhao Tang, Lucianna Kiffer, Giulia Fanti +1
Most permissionless blockchain networks run on peer-to-peer (P2P) networks, which offer flexibility and decentralization at the expense of performance (e.g., network latency). Hist…
RareGAN: Generating Samples for Rare Classes
Zinan Lin, Hao Liang, Giulia Fanti +1
We study the problem of learning generative adversarial networks (GANs) for a rare class of an unlabeled dataset subject to a labeling budget. This problem is motivated from practi…
FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning
Charlie Hou, Kiran K. Thekumparampil, Giulia Fanti +1
Federated learning (FL) aims to minimize the communication complexity of training a model over heterogeneous data distributed across many clients. A common approach is local method…
Abusive Language Detection in Heterogeneous Contexts: Dataset Collection and the Role of Supervised Attention
Hongyu Gong, Alberto Valido, Katherine M. Ingram +3
Abusive language is a massive problem in online social platforms. Existing abusive language detection techniques are particularly ill-suited to comments containing heterogeneous ab…
Self-Supervised Euphemism Detection and Identification for Content Moderation
Wanzheng Zhu, Hongyu Gong, Rohan Bansal +4
Fringe groups and organizations have a long history of using euphemisms--ordinary-sounding words with a secret meaning--to conceal what they are discussing. Nowadays, one common us…
The Effect of Network Topology on Credit Network Throughput
Vibhaalakshmi Sivaraman, Weizhao Tang, Shaileshh Bojja Venkatakrishnan +2
Credit networks rely on decentralized, pairwise trust relationships (channels) to exchange money or goods. Credit networks arise naturally in many financial systems, including the…
Efficient Algorithms for Federated Saddle Point Optimization
Charlie Hou, Kiran K. Thekumparampil, Giulia Fanti +1
We consider strongly convex-concave minimax problems in the federated setting, where the communication constraint is the main bottleneck. When clients are arbitrarily heterogeneous…
Why Spectral Normalization Stabilizes GANs: Analysis and Improvements
Zinan Lin, Vyas Sekar, Giulia Fanti
Spectral normalization (SN) is a widely-used technique for improving the stability and sample quality of Generative Adversarial Networks (GANs). However, there is currently limited…
SquirRL: Automating Attack Analysis on Blockchain Incentive Mechanisms with Deep Reinforcement Learning
Charlie Hou, Mingxun Zhou, Yan Ji +4
Incentive mechanisms are central to the functionality of permissionless blockchains: they incentivize participants to run and secure the underlying consensus protocol. Designing in…
Barracuda: The Power of -polling in Proof-of-Stake Blockchains
Giulia Fanti, Jiantao Jiao, Ashok Makkuva +3
A blockchain is a database of sequential events that is maintained by a distributed group of nodes. A key consensus problem in blockchains is that of determining the next block (da…
Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions
Zinan Lin, Alankar Jain, Chen Wang +2
Limited data access is a longstanding barrier to data-driven research and development in the networked systems community. In this work, we explore if and how generative adversarial…
Practical Low Latency Proof of Work Consensus
Lei Yang, Xuechao Wang, Vivek Bagaria +5
Bitcoin is the first fully-decentralized permissionless blockchain protocol to achieve a high level of security, but at the expense of poor throughput and latency. Scaling the perf…
Privacy-Utility Tradeoffs in Routing Cryptocurrency over Payment Channel Networks
Weizhao Tang, Weina Wang, Giulia Fanti +1
Payment channel networks (PCNs) are viewed as one of the most promising scalability solutions for cryptocurrencies today. Roughly, PCNs are networks where each node represents a us…
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs
Zinan Lin, Kiran Koshy Thekumparampil, Giulia Fanti +1
Disentangled generative models map a latent code vector to a target space, while enforcing that a subset of the learned latent codes are interpretable and associated with distinct…
Communication cost of consensus for nodes with limited memory
Giulia Fanti, Nina Holden, Yuval Peres +1
Motivated by applications in blockchains and sensor networks, we consider a model of nodes trying to reach consensus on their majority bit. Each node is assigned a bit at t…
Compounding of Wealth in Proof-of-Stake Cryptocurrencies
Giulia Fanti, Leonid Kogan, Sewoong Oh +3
Proof-of-stake (PoS) is a promising approach for designing efficient blockchains, where block proposers are randomly chosen with probability proportional to their stake. A primary…
High Throughput Cryptocurrency Routing in Payment Channel Networks
Vibhaalakshmi Sivaraman, Shaileshh Bojja Venkatakrishnan, Kathy Ruan +5
Despite growing adoption of cryptocurrencies, making fast payments at scale remains a challenge. Payment channel networks (PCNs) such as the Lightning Network have emerged as a via…
Dandelion++: Lightweight Cryptocurrency Networking with Formal Anonymity Guarantees
Giulia Fanti, Shaileshh Bojja Venkatakrishnan, Surya Bakshi +4
Recent work has demonstrated significant anonymity vulnerabilities in Bitcoin's networking stack. In particular, the current mechanism for broadcasting Bitcoin transactions allows…