Jakub Sliwinski

Jakub Sliwinski, Yann Vonlanthen, Roger Wattenhofer

Digital money can be implemented efficiently by avoiding consensus. However, no-consensus implementations have drawbacks, as they cannot support smart contracts, and (even more fundamentally) they cannot deal with conflicting transactions. We present a novel protocol that combines the benefits of an asynchronous, broadcast-based digital currency, with the capacity to perform consensus. This is achieved by selectively performing consensus a posteriori, i.e., only when absolutely necessary. Our on-demand consensus comes at the price of restricting the Byzantine participants to be less than a one-fifth minority in the system, which is the optimal threshold. We formally prove the correctness of our system and present an open-source implementation, which inherits many features from the Ethereum ecosystem.

Jakub Sliwinski, Roger Wattenhofer

We introduce a new permissionless blockchain architecture called ABC. ABC is completely asynchronous, and does rely on neither randomness nor proof-of-work. ABC can be parallelized, and transactions have finality within one round trip of communication. However, ABC satisfies only a relaxed form of consensus by introducing a weaker termination property. Without full consensus, ABC cannot support certain applications, in particular ABC cannot support general smart contracts. However, many important applications do not need general smart contracts, and ABC is a better solution for these applications. In particular, ABC can implement the functionality of a cryptocurrency like Bitcoin, replacing Bitcoin's energy-hungry proof-of-work with a proof-of-stake validation.

Georgia Avarikioti, Felix Laufenberg, Jakub Sliwinski et al.

Micropayment channels are the most prominent solution to the limitation on transaction throughput in current blockchain systems. However, in practice channels are risky because participants have to be online constantly to avoid fraud, and inefficient because participants have to open multiple channels and lock funds in them. To address the security issue, we propose a novel mechanism that involves watchtowers incentivized to watch the channels and reveal a fraud. Our protocol does not require participants to be online constantly watching the blockchain. The protocol is secure, incentive compatible and lightweight in communication. Furthermore, we present an adaptation of our protocol implementable on the Lightning protocol. Towards efficiency, we examine specific topological structures in the blockchain transaction graph and generalize the construction of channels to enable topologies better suited to specific real-world needs. In these cases, our construction reduces the required amount of signatures for a transaction and the total amount of locked funds in the system.

Nawal Benabbou, Mithun Chakraborty, Vinh Ho Xuan et al.

We introduce and analyze an extension to the matching problem on a weighted bipartite graph: Assignment with Type Constraints. The two parts of the graph are partitioned into subsets called types and blocks; we seek a matching with the largest sum of weights under the constraint that there is a pre-specified cap on the number of vertices matched in every type-block pair. Our primary motivation stems from the public housing program of Singapore, accounting for over 70% of its residential real estate. To promote ethnic diversity within its housing projects, Singapore imposes ethnicity quotas: each new housing development comprises blocks of flats and each ethnicity-based group in the population must not own more than a certain percentage of flats in a block. Other domains using similar hard capacity constraints include matching prospective students to schools or medical residents to hospitals. Limiting agents' choices for ensuring diversity in this manner naturally entails some welfare loss. One of our goals is to study the trade-off between diversity and social welfare in such settings. We first show that, while the classic assignment program is polynomial-time computable, adding diversity constraints makes it computationally intractable; however, we identify a $\tfrac{1}{2}$-approximation algorithm, as well as reasonable assumptions on the weights that permit poly-time algorithms. Next, we provide two upper bounds on the price of diversity -- a measure of the loss in welfare incurred by imposing diversity constraints -- as functions of natural problem parameters. We conclude the paper with simulations based on publicly available data from two diversity-constrained allocation problems -- Singapore Public Housing and Chicago School Choice -- which shed light on how the constrained maximization as well as lottery-based variants perform in practice.

Jakub Sliwinski, Martin Strobel, Yair Zick

We study the following problem: given a labeled dataset and a specific datapoint x, how did the i-th feature influence the classification for x? We identify a family of numerical influence measures - functions that, given a datapoint x, assign a numeric value phi_i(x) to every feature i, corresponding to how altering i's value would influence the outcome for x. This family, which we term monotone influence measures (MIM), is uniquely derived from a set of desirable properties, or axioms. The MIM family constitutes a provably sound methodology for measuring feature influence in classification domains; the values generated by MIM are based on the dataset alone, and do not make any queries to the classifier. While this requirement naturally limits the scope of our framework, we demonstrate its effectiveness on data.