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George Bissias

Researcher at University of Massachusetts Amherst

Publications -  33
Citations -  826

George Bissias is an academic researcher from University of Massachusetts Amherst. The author has contributed to research in topics: Differential privacy & Hash function. The author has an hindex of 11, co-authored 32 publications receiving 667 citations.

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Book ChapterDOI

Privacy vulnerabilities in encrypted HTTP streams

TL;DR: In this paper, the authors present a straightforward traffic analysis attack against encrypted HTTP streams that is surprisingly effective in identifying the source of the traffic, using a profile of the statistical characteristics of web requests from interesting sites, including distributions of packet sizes and inter-arrival times.
Proceedings ArticleDOI

Sybil-Resistant Mixing for Bitcoin

TL;DR: Xim is proposed, a two-party mixing protocol that is compatible with Bitcoin and related virtual currencies, and is the first decentralized protocol to simultaneously address Sybil attackers, denial-of-service attacks, and timing-based inference attacks.
Proceedings ArticleDOI

Surviving attacks on disruption-tolerant networks without authentication

TL;DR: It is concluded that disruption-tolerant networks are extremely robust to attack; in the trace-driven evaluations, an attacker that has compromised 30% of all nodes reduces delivery rates from 70% to 55%, and to 20% with knowledge of future events.
Proceedings ArticleDOI

Graphene: efficient interactive set reconciliation applied to blockchain propagation

TL;DR: This work introduces Graphene, a method and protocol for interactive set reconciliation among peers in blockchains and related distributed systems, and contributes a fast and implementation-independent algorithm for parameterizing an IBLT so that it is optimally small in size and meets a desired decode rate with arbitrarily high probability.
Book ChapterDOI

Graphene: A New Protocol for Block Propagation Using Set Reconciliation

TL;DR: A novel method of interactive set reconciliation for efficient block distribution that couples a Bloom filter with an IBLT and shows in simulation that Graphene reduces traffic overhead by reducing block overhead.