Mobile Encounter-based Social Sybil Control

Martinez, Martin; Hekmati, Arvin; Krishnamachari, Bhaskar; Yun, Seokgu

doi:10.1109/sds49854.2020.9143886

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…The main differences with the traditional decentralized protocols are the presence of the encryption and the total absence of a trusted central authority. Blockchain solutions for contact tracing have been recently addressed in [23], where the authors proposed that digitally signed pairwise encounters between mobile devices are stored in a distributed ledger and then some form of machine learning algorithms, such as deep learning networks, are used to identify the close social contacts.…”

Section: Terminology and Conceptual Definitionsmentioning

confidence: 99%

Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era

Shubina

Holcer

Gould

et al. 2020

Data

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Some of the recent developments in data science for worldwide disease control have involved research of large-scale feasibility and usefulness of digital contact tracing, user location tracking, and proximity detection on users’ mobile devices or wearables. A centralized solution relying on collecting and storing user traces and location information on a central server can provide more accurate and timely actions than a decentralized solution in combating viral outbreaks, such as COVID-19. However, centralized solutions are more prone to privacy breaches and privacy attacks by malevolent third parties than decentralized solutions, storing the information in a distributed manner among wireless networks. Thus, it is of timely relevance to identify and summarize the existing privacy-preserving solutions, focusing on decentralized methods, and analyzing them in the context of mobile device-based localization and tracking, contact tracing, and proximity detection. Wearables and other mobile Internet of Things devices are of particular interest in our study, as not only privacy, but also energy-efficiency, targets are becoming more and more critical to the end-users. This paper provides a comprehensive survey of user location-tracking, proximity-detection, and digital contact-tracing solutions in the literature from the past two decades, analyses their advantages and drawbacks concerning centralized and decentralized solutions, and presents the authors’ thoughts on future research directions in this timely research field.

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Section: Terminology and Conceptual Definitionsmentioning

confidence: 99%

Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era

Shubina

Holcer

Gould

et al. 2020

Data

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show abstract

“…Yet another approach may be to use location information or wireless signal strength as the basis for a Sybil control mechanism [35,36]. Another approach could be to use proof of social Contacts [37], which leverages encounter information between mobile nodes to detect and blacklist Sybil nodes.…”

Section: Mobile-device-oriented Sybil Controlmentioning

confidence: 99%

Blizzard: A Distributed Consensus Protocol for Mobile Devices

Kiamari,

Krishnamachari,

Yun

2024

Mathematics

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We present Blizzard, a Byzantine fault tolerant (BFT) distributed ledger protocol that is aimed at making mobile devices first-class citizens in the consensus process. Blizzard introduces a novel two-tier architecture by having the mobile nodes communicate through online brokers, and includes a decentralized matching scheme to ensure each node connects to a certain number of random brokers. Through mathematical analysis, we derive a guaranteed safety region (i.e., the set of ratios of malicious nodes and malicious brokers for which the safety is assured) for the Blizzard protocol. Liveness is shown as well. We analyze the performance of Blizzard in terms of its throughput, latency, and message complexity. Through experiments based on a software implementation, we show that Blizzard is capable of throughput on the order of several thousand transactions per second per shard and sub-second confirmation latency.

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“…Yet another approach may be to use location information or wireless signal strength as the basis for a Sybil control mechanism [30]- [31]. Another approach could be to use Proof of Social Contacts [32], which leverages encounter information between mobile nodes to detect and blacklist Sybil nodes.…”

Section: Mobile-device Oriented Sybil Controlmentioning

confidence: 99%

Blizzard: a Distributed Consensus Protocol for Mobile Devices

Kiamari¹,

Krishnamachari²,

Naveed³

et al. 2022

Preprint

Self Cite

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We present Blizzard, a Byzantine Fault Tolerant (BFT) distributed ledger protocol that is aimed at making mobile devices first-class citizens in the consensus process. Blizzard introduces a novel two-tier architecture by having the mobile nodes communicate through online brokers, and includes a decentralized matching scheme to ensure each node connects to a certain number of random brokers. Through mathematical analysis, we derive a guaranteed safety region (i.e. the set of ratios of malicious nodes and malicious brokers for which the safety is assured) for the Blizzard protocol. Liveness is shown as well. We analyze the performance of Blizzard in terms of its throughput, latency and message complexity. Through experiments based on a software implementation, we show that Blizzard is capable of throughput on the order of several thousand transactions per second per shard, and sub-second confirmation latency.

show abstract

Mobile Encounter-based Social Sybil Control

Cited by 6 publications

References 7 publications

Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era

Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era

Blizzard: A Distributed Consensus Protocol for Mobile Devices

Blizzard: a Distributed Consensus Protocol for Mobile Devices

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