40 research outputs found
Epidemic Information Diffusion: A Simple Solution to Support Community-based Recommendations in P2P Overlays
Epidemic protocols proved to be very efficient solutions for supporting
dynamic and complex information diffusion in highly dis- tributed computing
infrastructures, like P2P environments. They are useful bricks for building and
maintaining virtual network topologies, in the form of overlay networks as well
as to support pervasive diffusion of information when it is injected into the
network. This paper proposes a simple architecture exploiting the features of
epidemic approaches to foster a collaborative percolation of information
between computing nodes belonging to the network aimed at building a system
that groups similar users and spread useful information among them.Comment: 8 pages, 2 figure
Similitude:decentralised adaptation in large-scale P2P recommenders
Decentralised recommenders have been proposed to deliver privacy-preserving, personalised and highly scalable on-line recommendations. Current implementations tend, however, to rely on a hard-wired similarity metric that cannot adapt. This constitutes a strong limitation in the face of evolving needs. In this paper, we propose a framework to develop dynamically adaptive decentralised recommendation systems. Our proposal supports a decentralised form of adaptation, in which individual nodes can independently select, and update their own recommendation algorithm, while still collectively contributing to the overall systemâs mission. Keyword
D.1.3 â Protocols for emergent localities
GDD_HCERES2020This report presents two contributions that illustrate the potential of emerging-locality protocols in large-scale decentralized systems, in two areas of decentralized social computing: recommendation, and eventual consistency of mutable data structures. The first contribution consists of a framework supporting the development of dynamically adaptive decen-tralised recommendation systems. Decentralised recommenders have been proposed to deliver privacy-preserving, personalised and highly scalable on-line recommendations. Current implementations tend, however, to rely on a hard-wired similarity metric that cannot adapt. This constitutes a strong limitation in the face of evolving needs. Our framework address this through a decentralised form of adaptation, in which individual nodes can independently select, and update their own recommendation algorithm, while still collectively contributing to the overall system's mission. Our second contribution addresses the growing demand for differentiated consistency requirements in large-scale applications. A large number of today's applications rely on Eventual Consistency, a consistency model that emphasizes liveness over safety. Designers generally adopt this consistency model uniformly throughout a distributed system due to its ability to scale as the number of users or devices grows larger. But this clashes with the need for differentiated consistency requirements. In this contribution, we address this need by introducing UPS, a novel consistency mechanism that offers differentiated eventual consistency and delivery speed by working in pair with a two-phase epidemic broadcast protocol. We propose a closed-form analysis of our approach's delivery speed, and we evaluate our complete protocol experimentally on a simulated network of one million nodes. To measure the consistency trade-off, we formally define a novel and scalable consistency metric operating at runtime
Collaborating with Users in Proximity for Decentralized Mobile Recommender Systems
Typically, recommender systems from any domain, be it movies, music,
restaurants, etc., are organized in a centralized fashion. The service provider
holds all the data, biases in the recommender algorithms are not transparent to
the user, and the service providers often create lock-in effects making it
inconvenient for the user to switch providers. In this paper, we argue that the
user's smartphone already holds a lot of the data that feeds into typical
recommender systems for movies, music, or POIs. With the ubiquity of the
smartphone and other users in proximity in public places or public
transportation, data can be exchanged directly between users in a
device-to-device manner. This way, each smartphone can build its own database
and calculate its own recommendations. One of the benefits of such a system is
that it is not restricted to recommendations for just one user - ad-hoc group
recommendations are also possible. While the infrastructure for such a platform
already exists - the smartphones already in the palms of the users - there are
challenges both with respect to the mobile recommender system platform as well
as to its recommender algorithms. In this paper, we present a mobile
architecture for the described system - consisting of data collection, data
exchange, and recommender system - and highlight its challenges and
opportunities.Comment: Accepted for publication at the 2019 IEEE 16th International
Conference on Ubiquitous Intelligence and Computing (IEEE UIC 2019
Self-Healing Protocols for Connectivity Maintenance in Unstructured Overlays
In this paper, we discuss on the use of self-organizing protocols to improve
the reliability of dynamic Peer-to-Peer (P2P) overlay networks. Two similar
approaches are studied, which are based on local knowledge of the nodes' 2nd
neighborhood. The first scheme is a simple protocol requiring interactions
among nodes and their direct neighbors. The second scheme adds a check on the
Edge Clustering Coefficient (ECC), a local measure that allows determining
edges connecting different clusters in the network. The performed simulation
assessment evaluates these protocols over uniform networks, clustered networks
and scale-free networks. Different failure modes are considered. Results
demonstrate the effectiveness of the proposal.Comment: The paper has been accepted to the journal Peer-to-Peer Networking
and Applications. The final publication is available at Springer via
http://dx.doi.org/10.1007/s12083-015-0384-
On Democracy in Peer-to-Peer systems
The information flow inside a P2P network is highly dependent on the network
structure. In order to ease the diffusion of relevant data toward interested
peers, many P2P protocols gather similar nodes by putting them in direct
contact. With this approach the similarity between nodes is computed in a
point-to-point fashion: each peer individually identifies the nodes that share
similar interests with it. This leads to the creation of a sort of "private"
communities, limited to each peer neighbors list. This "private" knowledge do
not allow to identify the features needed to discover and characterize the
correlations that collect similar peers in broader groups. In order to let
these correlations to emerge, the collective knowledge of peers must be
exploited. One common problem to overcome in order to avoid the "private"
vision of the network, is related to how distributively determine the
representation of a community and how nodes may decide to belong to it. We
propose to use a gossip-like approach in order to let peers elect and identify
leaders of interest communities. Once leaders are elected, their profiles are
used as community representatives. Peers decide to adhere to a community or
another by choosing the most similar representative they know about
D.1.3 â Protocols for emergent localities
GDD_HCERES2020This report presents two contributions that illustrate the potential of emerging-locality protocols in large-scale decentralized systems, in two areas of decentralized social computing: recommendation, and eventual consistency of mutable data structures. The first contribution consists of a framework supporting the development of dynamically adaptive decen-tralised recommendation systems. Decentralised recommenders have been proposed to deliver privacy-preserving, personalised and highly scalable on-line recommendations. Current implementations tend, however, to rely on a hard-wired similarity metric that cannot adapt. This constitutes a strong limitation in the face of evolving needs. Our framework address this through a decentralised form of adaptation, in which individual nodes can independently select, and update their own recommendation algorithm, while still collectively contributing to the overall system's mission. Our second contribution addresses the growing demand for differentiated consistency requirements in large-scale applications. A large number of today's applications rely on Eventual Consistency, a consistency model that emphasizes liveness over safety. Designers generally adopt this consistency model uniformly throughout a distributed system due to its ability to scale as the number of users or devices grows larger. But this clashes with the need for differentiated consistency requirements. In this contribution, we address this need by introducing UPS, a novel consistency mechanism that offers differentiated eventual consistency and delivery speed by working in pair with a two-phase epidemic broadcast protocol. We propose a closed-form analysis of our approach's delivery speed, and we evaluate our complete protocol experimentally on a simulated network of one million nodes. To measure the consistency trade-off, we formally define a novel and scalable consistency metric operating at runtime
Hide & Share: Landmark-based Similarity for Private KNN Computation
International audienceComputing k-nearest-neighbor graphs constitutes a fundamental operation in a variety of data-mining applications. As a prominent example, user-based collaborative-filtering provides recommendations by identifying the items appreciated by the closest neighbors of a target user. As this kind of applications evolve, they will require KNN algorithms to operate on more and more sensitive data. This has prompted researchers to propose decentralized peer-to-peer KNN solutions that avoid concentrating all information in the hands of one central organization. Unfortunately , such decentralized solutions remain vulnerable to malicious peers that attempt to collect and exploit information on participating users. In this paper, we seek to overcome this limitation by proposing H&S (Hide & Share), a novel landmark-based similarity mechanism for decentralized KNN computation. Landmarks allow users (and the associated peers) to estimate how close they lay to one another without disclosing their individual profiles. We evaluate H&S in the context of a user-based collaborative-filtering recommender with publicly available traces from existing recommendation systems. We show that although landmark-based similarity does disturb similarity values (to ensure privacy), the quality of the recommendations is not as significantly hampered. We also show that the mere fact of disturbing similarity values turns out to be an asset because it prevents a malicious user from performing a profile reconstruction attack against other users, thus reinforcing users' privacy. Finally, we provide a formal privacy guarantee by computing an upper bound on the amount of information revealed by H&S about a user's profile