07271 Abstracts Collection -- Computational Social Systems and the Internet

From 01.07. to 06.07.2007, the Dagstuhl Seminar 07271 ``Computational Social Systems and the Internet\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

05011 Abstracts Collection -- Computing and Markets

From 03.01.05 to 07.01.05, the Dagstuhl Seminar 05011``Computing and Markets\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Learning Optimal Deterministic Auctions with Correlated Valuation Distributions

In mechanism design, it is challenging to design the optimal auction with correlated values in general settings. Although value distribution can be further exploited to improve revenue, the complex correlation structure makes it hard to acquire in practice. Data-driven auction mechanisms, powered by machine learning, enable to design auctions directly from historical auction data, without relying on specific value distributions. In this work, we design a learning-based auction, which can encode the correlation of values into the rank score of each bidder, and further adjust the ranking rule to approach the optimal revenue. We strictly guarantee the property of strategy-proofness by encoding game theoretical conditions into the neural network structure. Furthermore, all operations in the designed auctions are differentiable to enable an end-to-end training paradigm. Experimental results demonstrate that the proposed auction mechanism can represent almost any strategy-proof auction mechanism, and outperforms the auction mechanisms wildly used in the correlated value settings

On Optimizing Transaction Fees in Bitcoin using AI: Investigation on Miners Inclusion Pattern

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Internet Technology, https://doi.org/10.1145/3528669.The transaction-rate bottleneck built into popular proof-of-work-based cryptocurrencies, like Bitcoin and Ethereum, leads to fee markets where transactions are included according to a first-price auction for block space. Many attempts have been made to adjust and predict the fee volatility, but even well-formed transactions sometimes experience unexpected delays and evictions unless a substantial fee is offered. In this paper, we propose a novel transaction inclusion model that describes the mechanisms and patterns governing miners decisions to include individual transactions in the Bitcoin system. Using this model we devise a Machine Learning (ML) approach to predict transaction inclusion. We evaluate our predictions method using historical observations of the Bitcoin network from a five month period that includes more than 30 million transactions and 120 million entries. We find that our Machine Learning (ML) model can predict fee volatility with an accuracy of up to 91%. Our findings enable Bitcoin users to improve their fee expenses and the approval time for their transactions

Research on efficiency and privacy issues in wireless communication

Wireless spectrum is a limited resource that must be used efficiently. It is also a broadcast medium, hence, additional procedures are required to maintain communication over the wireless spectrum private. In this thesis, we investigate three key issues related to efficient use and privacy of wireless spectrum use. First, we propose GAVEL, a truthful short-term auction mechanism that enables efficient use of the wireless spectrum through the licensed shared access model. Second, we propose CPRecycle, an improved Orthogonal Frequency Division Multiplexing (OFDM) receiver that retrieves useful information from the cyclic prefix for interference mitigation thus improving spectral efficiency. Third and finally, we propose WiFi Glass, an attack vector on home WiFi networks to infer private information about home occupants. First we consider, spectrum auctions. Existing short-term spectrum auctions do not satisfy all the features required for a heterogeneous spectrum market. We discover that this is due to the underlying auction format, the sealed bid auction. We propose GAVEL, a truthful auction mechanism, that is based on the ascending bid auction format, that avoids the pitfalls of existing auction mechanisms that are based on the sealed bid auction format. Using extensive simulations we observe that GAVEL can achieve better performance than existing mechanisms. Second, we study the use of cyclic prefix in Orthogonal Frequency Division Multiplexing. The cyclic prefix does contain useful information in the presence of interference. We discover that while the signal of interest is redundant in the cyclic prefix, the interference component varies significantly. We use this insight to design CPRecycle, an improved OFDM receiver that is capable of using the information in the cyclic prefix to mitigate various types of interference. It improves spectral efficiency by decoding packets in the presence of interference. CPRecycle require changes to the OFDM receiver and can be deployed in most networks today. Finally, home WiFi networks are considered private when encryption is enabled using WPA2. However, experiments conducted in real homes, show that the wireless activity on the home network can be used to infer occupancy and activity states such as sleeping and watching television. With this insight, we propose WiFi Glass, an attack vector that can be used to infer occupancy and activity states (limited to three activity classes), using only the passively sniffed WiFi signal from the home environment. Evaluation with real data shows that in most of the cases, only about 15 minutes of sniffed WiFi signal is required to infer private information, highlighting the need for countermeasures