References of "Pervasive and Mobile Computing"
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See detailReCon: Sybil-Resistant Consensus from Reputation
Biryukov, Alex UL; Feher, Daniel UL

in Pervasive and Mobile Computing (2019)

In this paper we describe how to couple reputation systems with distributed consensus protocols to provide a scalable permissionless consensus protocol with a low barrier of entry, while still providing ... [more ▼]

In this paper we describe how to couple reputation systems with distributed consensus protocols to provide a scalable permissionless consensus protocol with a low barrier of entry, while still providing strong resistance against Sybil attacks for large peer-to-peer networks of untrusted validators. We introduce reputation module ReCon, which can be laid on top of various consensus protocols such as PBFT or HoneyBadger. The protocol takes external reputation ranking as input and then ranks nodes based on the outcomes of consensus rounds run by a small committee, and adaptively selects the committee based on the current reputation. ReCon can tolerate larger threshold of malicious nodes (up to slightly above 1/2) compared to the 1/3 limit of BFT consensus algorithms. [less ▲]

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See detailSecurity and Privacy of Mobile Wallet Users in Bitcoin, Dash, Monero, and Zcash
Biryukov, Alex UL; Tikhomirov, Sergei UL

in Pervasive and Mobile Computing (2019)

Mobile devices play an increasingly important role in the cryptocurrency ecosystem, yet their privacy guarantees remain unstudied. To verify transactions, they either trust a server or use simple payment ... [more ▼]

Mobile devices play an increasingly important role in the cryptocurrency ecosystem, yet their privacy guarantees remain unstudied. To verify transactions, they either trust a server or use simple payment verification. First, we review the security and privacy of popular Android wallets for Bitcoin and the three major privacy-focused cryptocurrencies (Dash, Monero, Zcash). Then, we investigate the network-level properties of cryptocurrencies and propose a method of transaction clustering based on timing analysis. We implement and test our method on selected wallets and show that a moderately resourceful attacker can correlate transactions issued from one device with relatively high accuracy. [less ▲]

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See detailWhy Energy Matters? Profiling Energy Consumption of Mobile Crowdsensing Data Collection Frameworks
Tomasoni, Mattia; Capponi, Andrea UL; Fiandrino, Claudio UL et al

in Pervasive and Mobile Computing (2018)

Mobile Crowdsensing (MCS) has emerged in the last years and has become one of the most prominent paradigms for urban sensing. The citizens actively participate in the sensing process by contributing data ... [more ▼]

Mobile Crowdsensing (MCS) has emerged in the last years and has become one of the most prominent paradigms for urban sensing. The citizens actively participate in the sensing process by contributing data with their mobile devices. To produce data, citizens sustain costs, i.e., the energy consumed for sensing and reporting operations. Hence, devising energy efficient data collection frameworks (DCF) is essential to foster participation. In this work, we investigate from an energy-perspective the performance of different DCFs. Our methodology is as follows: (i) we developed an Android application that implements the DCFs, (ii) we profiled the energy and network performance with a power monitor and Wireshark, (iii) we included the obtained traces into CrowdSenSim simulator for large-scale evaluations in city-wide scenarios such as Luxembourg, Turin and Washington DC. The amount of collected data, energy consumption and fairness are the performance indexes evaluated. The results unveil that DCFs with continuous data reporting are more energy-efficient and fair than DCFs with probabilistic reporting. The latter exhibit high variability of energy consumption, i.e., to produce the same amount of data, the associated energy cost of different users can vary significantly. [less ▲]

Detailed reference viewed: 127 (7 UL)