arXiv subjects

Tiantian Gong

Publications and source records attributed to Tiantian Gong.

6 recordsLinked to original sources

arXiv2025

More is Merrier: Relax the Non-Collusion Assumption in Multi-Server PIR

A long line of research on secure computation has confirmed that anything that can be computed, can be computed securely using a set of non-colluding parties. Indeed, this non-collusion assumption makes a number of problems solvable, as well as reduces overheads and bypasses computational hardness results, and it is pervasive across different privacy-enhancing technologies. However, it remains highly susceptible to covert, undetectable collusion among computing parties. This work stems from an observation that if the number of available computing parties is much higher than the number of parties required to perform a secure computation task, collusion attempts in privacy-preserving computations could be deterred. We focus on the prominent privacy-preserving computation task of multi-server $1$-private information retrieval (PIR) that inherently assumes no pair-wise collusion. For PIR application scenarios, such as those for blockchain light clients, where the available servers can be plentiful, a single server's deviating action is not tremendously beneficial to itself. We can make deviations undesired via small amounts of rewards and penalties, thus significantly raising the bar for collusion resistance. We design and implement a collusion mitigation mechanism on a public bulletin board with payment execution functions, considering only rational and malicious parties with no honest non-colluding servers. Privacy protection is offered for an extended period after the query executions.

cs.CR↗

arXiv2025

V3rified: Revelation vs Non-Revelation Mechanisms for Decentralized Verifiable Computation

In the era of Web3, decentralized technologies have emerged as the cornerstone of a new digital paradigm. Backed by a decentralized blockchain architecture, the Web3 space aims to democratize all aspects of the web. From data-sharing to learning models, outsourcing computation is an established, prevalent practice. Verifiable computation makes this practice trustworthy as clients/users can now efficiently validate the integrity of a computation. As verifiable computation gets considered for applications in the Web3 space, decentralization is crucial for system reliability, ensuring that no single entity can suppress clients. At the same time, however, decentralization needs to be balanced with efficiency: clients want their computations done as quickly as possible. Motivated by these issues, we study the trade-off between decentralization and efficiency when outsourcing computational tasks to strategic, rational solution providers. Specifically, we examine this trade-off when the client employs (1) revelation mechanisms, i.e. auctions, where solution providers bid their desired reward for completing the task by a specific deadline and then the client selects which of them will do the task and how much they will be rewarded, and (2) simple, non-revelation mechanisms, where the client commits to the set of rules she will use to map solutions at specific times to rewards and then solution providers decide whether they want to do the task or not. We completely characterize the power and limitations of revelation and non-revelation mechanisms in our model.

cs.GT↗

arXiv2025

The Case of FBA as a DEX Processing Model

We investigate the welfare loss of continuous and discrete order matching models in blockchain-based decentralized exchanges (DEX) that utilize order books to record outstanding orders. Continuous processing matches each incoming transaction against the current order book. The discrete processing model, i.e., frequent batch auction (FBA), executes transactions discretely in batches with a uniform price double auction: Orders are first matched according to price, then the exact transaction order if competing orders specify the same price. We find that FBA imposes less welfare loss and provides better liquidity than continuous processing in typical scenarios, e.g., when few parties are privately informed about asset valuations. Even otherwise, it achieves better social welfare and liquidity provision in the following settings: when price takers and public information reflecting asset value changes arrive sufficiently frequently compared to private information, when the priority fees (for faster transaction inclusion into blockchains) are small, or when the market is more balanced on both buy and sell sides. Our empirical analysis on the BTC-USD and ETH-USD transactions on a DEX named dYdX indicates that FBA can reduce transaction costs by $21\%-37\%$.

cs.CR↗

arXiv2023

Prototype-guided Cross-modal Completion and Alignment for Incomplete Text-based Person Re-identification

Traditional text-based person re-identification (ReID) techniques heavily rely on fully matched multi-modal data, which is an ideal scenario. However, due to inevitable data missing and corruption during the collection and processing of cross-modal data, the incomplete data issue is usually met in real-world applications. Therefore, we consider a more practical task termed the incomplete text-based ReID task, where person images and text descriptions are not completely matched and contain partially missing modality data. To this end, we propose a novel Prototype-guided Cross-modal Completion and Alignment (PCCA) framework to handle the aforementioned issues for incomplete text-based ReID. Specifically, we cannot directly retrieve person images based on a text query on missing modality data. Therefore, we propose the cross-modal nearest neighbor construction strategy for missing data by computing the cross-modal similarity between existing images and texts, which provides key guidance for the completion of missing modal features. Furthermore, to efficiently complete the missing modal features, we construct the relation graphs with the aforementioned cross-modal nearest neighbor sets of missing modal data and the corresponding prototypes, which can further enhance the generated missing modal features. Additionally, for tighter fine-grained alignment between images and texts, we raise a prototype-aware cross-modal alignment loss that can effectively reduce the modality heterogeneity gap for better fine-grained alignment in common space. Extensive experimental results on several benchmarks with different missing ratios amply demonstrate that our method can consistently outperform state-of-the-art text-image ReID approaches.

cs.CV↗

arXiv2023

Front-running Attack in Sharded Blockchains and Fair Cross-shard Consensus

Sharding is a prominent technique for scaling blockchains. By dividing the network into smaller components known as shards, a sharded blockchain can process transactions in parallel without introducing inconsistencies through the coordination of intra-shard and cross-shard consensus protocols. However, we observe a critical security issue with sharded systems: transaction ordering manipulations can occur when coordinating intra-shard and cross-shard consensus protocols, leaving the system vulnerable to attack. Specifically, we identify a novel security issue known as finalization fairness, which can be exploited through a front-running attack. This attack allows an attacker to manipulate the execution order of transactions, even if the victim's transaction has already been processed and added to the blockchain by a fair intra-shard consensus. To address the issue, we offer Haechi, a novel cross-shard protocol that is immune to front-running attacks. Haechi introduces an ordering phase between transaction processing and execution, ensuring that the execution order of transactions is the same as the processing order and achieving finalization fairness. To accommodate different consensus speeds among shards, Haechi incorporates a finalization fairness algorithm to achieve a globally fair order with minimal performance loss. By providing a global order, Haechi ensures strong consistency among shards, enabling better parallelism in handling conflicting transactions across shards. These features make Haechi a promising solution for supporting popular smart contracts in the real world. To evaluate Haechi's performance, we implemented the protocol using Tendermint and conducted extensive experiments on a geo-distributed AWS environment. Our results demonstrate that Haechi achieves finalization fairness with little performance sacrifice compared to existing cross-shard consensus protocols.

cs.CR↗

arXiv2022

Towards Overcoming the Undercutting Problem

Mining processes of Bitcoin and similar cryptocurrencies are currently incentivized with voluntary transaction fees and fixed block rewards which will halve gradually to zero. In the setting where optional and arbitrary transaction fee becomes the remaining incentive, Carlsten et al.\ [CCS~2016] find that an undercutting attack can become the equilibrium strategy for miners. In undercutting, the attacker deliberately forks an existing chain by leaving wealthy transactions unclaimed to attract petty complaint miners to its fork. We observe that two simplifying assumptions in [CCS~2016] of fees arriving at fixed rates and miners collecting {\em all} accumulated fees regardless of block size limit are often infeasible in practice and find that they are inaccurately inflating the profitability of undercutting. Studying Bitcoin and Monero blockchain data, we find that the fees deliberately left out by an undercutter may not be attractive to other miners (hence to the attacker itself): the deliberately left out transactions may not fit into a new block without "squeezing out" some other to-be transactions, and thus claimable fees in the next round cannot be raised arbitrarily. This work views undercutting and shifting among chains rationally as mining strategies of rational miners. We model profitability of undercutting strategy with block size limit present, which bounds the claimable fees in a round and gives rise to a pending (cushion) transaction set. In the proposed model, we first identify the conditions necessary to make undercutting profitable. We then present an easy-to-deploy defense against undercutting by selectively assembling transactions into the new block to invalidate the identified conditions. Under a typical setting with undercutters present, applying this avoidance technique is a Nash Equilibrium. Finally, we complement the above analytical results with experiments.

cs.CR↗