scholarly journals Comprehensive Anonymity Trilemma: User Coordination is not enough

2020 ◽  
Vol 2020 (3) ◽  
pp. 356-383
Author(s):  
Debajyoti Das ◽  
Sebastian Meiser ◽  
Esfandiar Mohammadi ◽  
Aniket Kate
Keyword(s):  
Communication Networks ◽  
Formal Analysis ◽  
Impossibility Result ◽  
Lessons Learned ◽  
Anonymous Communication ◽  
Low Latency ◽  
Careful Observation ◽  
Bandwidth Overhead ◽  
Relevant Class

AbstractFor anonymous communication networks (ACNs), Das et al. recently confirmed a long-suspected trilemma result that ACNs cannot achieve strong anonymity, low latency overhead and low bandwidth overhead at the same time. Our paper emanates from the careful observation that their analysis does not include a relevant class of ACNs with what we call user coordination where users proactively work together towards improving their anonymity. We show that such protocols can achieve better anonymity than predicted by the above trilemma result. As the main contribution, we present a stronger impossibility result that includes all ACNs we are aware of. Along with our formal analysis, we provide intuitive interpretations and lessons learned. Finally, we demonstrate qualitatively stricter requirements for the Anytrust assumption (all but one protocol party is compromised) prevalent across ACNs.

scholarly journals On Privacy Notions in Anonymous Communication

2019 ◽  
Vol 2019 (2) ◽  
pp. 105-125 ◽  
Author(s):  
Christiane Kuhn ◽  
Martin Beck ◽  
Stefan Schiffner ◽  
Eduard Jorswieck ◽  
Thorsten Strufe
Keyword(s):  
Communication Networks ◽  
Ad Hoc ◽  
Building Blocks ◽  
Anonymous Communication ◽  
Formal Definition ◽  
Formal Definitions ◽  
Definition Of

Abstract Many anonymous communication networks (ACNs) with different privacy goals have been developed. Still, there are no accepted formal definitions of privacy goals, and ACNs often define their goals ad hoc. However, the formal definition of privacy goals benefits the understanding and comparison of different flavors of privacy and, as a result, the improvement of ACNs. In this paper, we work towards defining and comparing privacy goals by formalizing them as privacy notions and identifying their building blocks. For any pair of notions we prove whether one is strictly stronger, and, if so, which. Hence, we are able to present a complete hierarchy. Using this rigorous comparison between notions, we revise inconsistencies between the existing works and improve the understanding of privacy goals.

scholarly journals The most important things I have learnt in my career as a psychiatrist

2017 ◽  
Vol 41 (1) ◽  
pp. 51-55
Author(s):  
Julian Leff
Keyword(s):  
Psychiatric Symptoms ◽  
Clinical Work ◽  
Lessons Learned ◽  
Dropping Out ◽  
Solid Base ◽  
Careful Observation ◽  
Somatic Condition ◽  
Research Activities

SummaryThis paper is something of a patchwork, incorporating many issues that have intrigued me during 34 years of research. I have included the importance of maintaining a solid base in clinical work, alongside research activities, and being alert to the possibility of a somatic condition contributing to psychiatric symptoms. I stress the value of careful observation of patients, their response to treatments and reasons for dropping out. In addition, I have included 14 more lessons, learned from my experience of research, which I hope will be of use to those readers who aspire to become researchers.

Detection and analysis of eavesdropping in anonymous communication networks

2014 ◽  
Vol 14 (3) ◽  
pp. 205-220 ◽  
Author(s):  
Sambuddho Chakravarty ◽  
Georgios Portokalidis ◽  
Michalis Polychronakis ◽  
Angelos D. Keromytis
Keyword(s):  
Communication Networks ◽  
Anonymous Communication

scholarly journals Communication and Networking Technologies for UAVs: A Survey

2020 ◽  
Author(s):  
chathuranga basnayaka
Keyword(s):  
Machine Learning ◽  
Power Management ◽  
Lessons Learned ◽  
Low Latency ◽  
Focal Points ◽  
Future Directions ◽  
Vehicle Communication ◽  
Work Related ◽  
Aerial Vehicle ◽  
Networking Technologies

<div>With the advancement in drone technology;</div><div>in just a few years; drones will be assisting humans</div><div>in every domain: But there are many challenges to</div><div>be tackled; communication being the chief one: This</div><div>paper aims at providing insights into the latest UAV</div><div>(Unmanned Aerial Vehicle ) communication technolo-</div><div>gies through investigation of suitable task modules;</div><div>antennas; resource handling platforms; and network</div><div>architectures: Additionally; we explore techniques such</div><div>as machine learning and path planning to enhance exist-</div><div>ing drone communication methods:Encryption and opti-</div><div>mization techniques for ensuring long􀀀lasting and se-</div><div>cure communications; as well as for power management;</div><div>are discussed:Moreover; applications of UAV networks</div><div>for di?erent contextual uses ranging from navigation to</div><div>surveillance; URLLC (Ultra-reliable and low􀀀latency</div><div>communications); edge computing and work related</div><div>to arti?cial intelligence are examined: In particular;</div><div>the intricate interplay between UAV; advanced cellu-</div><div>lar communication; and internet of things constitutes</div><div>one of the focal points of this paper: The survey en-</div><div>compasses lessons learned; insights; challenges; open</div><div>issues; and future directions in UAV communications:</div>

scholarly journals Privacy Preserving Detection of Path Bias Attacks in Tor

2020 ◽  
Vol 2020 (4) ◽  
pp. 111-130
Author(s):  
Lauren Watson ◽  
Anupam Mediratta ◽  
Tariq Elahi ◽  
Rik Sarkar
Keyword(s):  
Data Collection ◽  
Communication Networks ◽  
Privacy Preserving ◽  
Anonymous Communication ◽  
Sample Complexity ◽  
Entry And Exit ◽  
Telemetry Data ◽  
Detection Process ◽  
Process Simulations ◽  
Efficient Scheme

AbstractAnonymous communication networks like Tor are vulnerable to attackers that control entry and exit nodes. Such attackers can compromise the essential anonymity and privacy properties of the network. In this paper, we consider the path bias attack– where the attacker induces a client to use compromised nodes and thus links the client to their destination. We describe an efficient scheme that detects such attacks in Tor by collecting routing telemetry data from nodes in the network. The data collection is differentially private and thus does not reveal behaviour of individual users even to nodes within the network. We show provable bounds for the sample complexity of the scheme and describe methods to make it resilient to introduction of false data by the attacker to subvert the detection process. Simulations based on real configurations of the Tor network show that the method works accurately in practice.

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