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Recent Submissions

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Optimistic Fast Confirmation While Tolerating Malicious Majority in Blockchains
(2023-04-06) Hou, Ruomu; Yu, Haifeng
The robustness of a blockchain against the adversary is often characterized by the maximum fraction (fmax) of adversarial power that it can tolerate. While most existing blockchains can only tolerate fmax < 0.5 or lower, there are some blockchain systems that are able to tolerate a malicious majority, namely fmax >= 0.5. A key price paid by such blockchains, however, is their large confirmation latency. This work aims to significantly reduce the confirmation latency in such blockchains, under the common case where the actual fraction f of adversarial power is relatively small. To this end, we propose a novel blockchain called FLINT. FLINT tolerates fmax >= 0.5 and can give optimistic execution (i.e., fast confirmation) whenever f is relatively small. Our experiments show that the fast confirmation in FLINT only takes a few minutes, as compared to several hours of confirmation latency in prior works.
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Achieving Sublinear Complexity under Constant 𝑇 in 𝑇 -interval Dynamic Networks
(2022-05-26) Hou, Ruomu; Jahja, Irvan; Sun, Yucheng; Wu, Jiyan; Yu, Haifeng
This paper considers standard {\em $T$-interval dynamic networks}, where the $N$ nodes in the network proceed in lock-step {\em rounds}, and where the topology of the network can change arbitrarily from round to round, as determined by an {\em adversary}. The adversary promises that in every $T$ consecutive rounds, the $T$ (potentially different) topologies in those $T$ rounds contain a common connected subgraph that spans all nodes. Within such a context, we propose novel algorithms for solving some fundamental distributed computing problems such as Count/Consensus/Max. Our algorithms are the first algorithms whose complexities do not contain an $\Omega(N)$ term, under constant $T$ values. Previous sublinear algorithms require significantly larger $T$ values.
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Survey on Data Quality and Provenance
(2021-11) Schmitz, Martin
This technical report summarizes research on data quality, provenance and truth discovery from the last decades. It examines opportunities to use machine learning methods to enhance data quality and provenance. This report can serve as a starting point to nd the key publications of the topics "provenance" and "data quality" and to do further research in those areas in general as well as in combination with machine learning algorithms.
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Temporal Keyword Search with Aggregates and Group-By
(2021-07) Gao, Qiao; Lee, Mong Li; Ling, Tok Wang
Abstract. Temporal keyword search enables non-expert users to query temporal relational databases with time conditions. However, aggregates and group-by are currently not supported in temporal keyword search, which hinders querying of statistical information in temporal databases. This work proposes a framework to support aggregate, group-by and time condition in temporal keyword search. We observe that simply combining non-temporal keyword search with aggregates, group-by, and temporal aggregate operators may lead to incorrect and meaningless results as a result of data duplication over time periods. As such, our framework utilizes Object-Relationship-Attribute semantics to identify a unique at-tribute set in the join sequence relation and remove data duplicates from this attribute set to ensure the correctness of aggregate and group-by computation. We also consider the time period in which temporal at-tributes occur when computing aggregate to return meaningful results. Experiment results demonstrate the importance of these steps to retrieve correct results for keyword queries over temporal databases.
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On the Power of Randomization in Distributed Algorithms in Dynamic Networks with Adaptive Adversaries*
(2020-09-16) Jahja, Irvan; Yu, Haifeng; Hou, Ruomu
This paper investigates the power of randomization in general distributed algorithms in dynamic networks where the network’s topology may evolve over time, as determined by some adaptive adversary. In such a context, randomization may help algorithms to better deal with i) “bad” inputs to the algorithm, and ii) evolving topologies generated by “bad” adaptive adversaries. We prove that randomness offers limited power to better deal with “bad” adaptive adversary. We define a simple notion of prophetic adversary for determining the evolving topologies. Such an adversary accurately predicts all randomness in the algorithm beforehand, and hence the randomness will be useless against “bad” prophetic adversaries. Given a randomized algorithm P whose time complexity satisfies some mild conditions, we prove that P can always be converted to a new algorithm Q with comparable time complexity, even when Q runs against prophetic adversaries. This