15,391 research outputs found
Octopus: A Secure and Anonymous DHT Lookup
Distributed Hash Table (DHT) lookup is a core technique in structured
peer-to-peer (P2P) networks. Its decentralized nature introduces security and
privacy vulnerabilities for applications built on top of them; we thus set out
to design a lookup mechanism achieving both security and anonymity, heretofore
an open problem. We present Octopus, a novel DHT lookup which provides strong
guarantees for both security and anonymity. Octopus uses attacker
identification mechanisms to discover and remove malicious nodes, severely
limiting an adversary's ability to carry out active attacks, and splits lookup
queries over separate anonymous paths and introduces dummy queries to achieve
high levels of anonymity. We analyze the security of Octopus by developing an
event-based simulator to show that the attacker discovery mechanisms can
rapidly identify malicious nodes with low error rate. We calculate the
anonymity of Octopus using probabilistic modeling and show that Octopus can
achieve near-optimal anonymity. We evaluate Octopus's efficiency on Planetlab
with 207 nodes and show that Octopus has reasonable lookup latency and
manageable communication overhead
Peer to Peer Information Retrieval: An Overview
Peer-to-peer technology is widely used for file sharing. In the past decade a number of prototype peer-to-peer information retrieval systems have been developed. Unfortunately, none of these have seen widespread real- world adoption and thus, in contrast with file sharing, information retrieval is still dominated by centralised solutions. In this paper we provide an overview of the key challenges for peer-to-peer information retrieval and the work done so far. We want to stimulate and inspire further research to overcome these challenges. This will open the door to the development and large-scale deployment of real-world peer-to-peer information retrieval systems that rival existing centralised client-server solutions in terms of scalability, performance, user satisfaction and freedom
Knowledge is at the Edge! How to Search in Distributed Machine Learning Models
With the advent of the Internet of Things and Industry 4.0 an enormous amount
of data is produced at the edge of the network. Due to a lack of computing
power, this data is currently send to the cloud where centralized machine
learning models are trained to derive higher level knowledge. With the recent
development of specialized machine learning hardware for mobile devices, a new
era of distributed learning is about to begin that raises a new research
question: How can we search in distributed machine learning models? Machine
learning at the edge of the network has many benefits, such as low-latency
inference and increased privacy. Such distributed machine learning models can
also learn personalized for a human user, a specific context, or application
scenario. As training data stays on the devices, control over possibly
sensitive data is preserved as it is not shared with a third party. This new
form of distributed learning leads to the partitioning of knowledge between
many devices which makes access difficult. In this paper we tackle the problem
of finding specific knowledge by forwarding a search request (query) to a
device that can answer it best. To that end, we use a entropy based quality
metric that takes the context of a query and the learning quality of a device
into account. We show that our forwarding strategy can achieve over 95%
accuracy in a urban mobility scenario where we use data from 30 000 people
commuting in the city of Trento, Italy.Comment: Published in CoopIS 201
- …