Exploiting the Synergy Between Gossiping and Structured Overlays

In this position paper we argue for exploiting the synergy between gossip-based algorithms and structured overlay networks (SON). These two strands of research have both aimed at building fault-tolerant, dynamic, self-managing, and large-scale distributed systems. Despite the common goals, the two areas have, however, been relatively isolated. We focus on three problem domains where there is an untapped potential of using gossiping combined with SONs. We argue for applying gossip-based membership for ring-based SONs---such as Chord and Bamboo---to make them handle partition mergers and loopy networks. We argue that small world SONs---such as Accordion and Mercury---are specifically well-suited for gossip-based membership management. The benefits would be better graph-theoretic properties. Finally, we argue that gossip-based algorithms could use the overlay constructed by SONs. For example, many unreliable broadcast algorithms for SONs could be augmented with anti-entropy protocols. Similarly, gossip-based aggregation could be used in SONs for network size estimation and load-balancing purposes

Socially-Aware Distributed Hash Tables for Decentralized Online Social Networks

Many decentralized online social networks (DOSNs) have been proposed due to an increase in awareness related to privacy and scalability issues in centralized social networks. Such decentralized networks transfer processing and storage functionalities from the service providers towards the end users. DOSNs require individualistic implementation for services, (i.e., search, information dissemination, storage, and publish/subscribe). However, many of these services mostly perform social queries, where OSN users are interested in accessing information of their friends. In our work, we design a socially-aware distributed hash table (DHTs) for efficient implementation of DOSNs. In particular, we propose a gossip-based algorithm to place users in a DHT, while maximizing the social awareness among them. Through a set of experiments, we show that our approach reduces the lookup latency by almost 30% and improves the reliability of the communication by nearly 10% via trusted contacts.Comment: 10 pages, p2p 2015 conferenc

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

Small-World Networks: Is there a mismatch between theory and practice?

In small-world networks, each peer is connected to its closest neighbors in the network topology, as well as to additional long-range contact(s), also called shortcut(s). In 2000, Kleinberg showed that greedy routing in a $n$ peer small-world network, performs in $O(n^\frac{1}{3})$ steps when the distance to shortcuts is chosen uniformly at random, and in $O(\log^2n)$ when the distance to shortcuts is chosen according to a harmonic distribution in a $d$-dimensional mesh. Yet, we observe through experimental results that peer to peer gossip-based protocols achieving small-world topologies where shortcuts are randomly chosen, perform well in practice. The motivation of this paper is to explore this mismatch and attempt to reconcile theory and practice in the context of small-world overlay networks. More precisely, based on the observation that, despite the fact that the routing complexity of gossip-based small-world overlay networks is not polylogarithmic (as proved by Kleinberg), this type of networks ultimately provide reasonable results in practice. This leads us to think that the asymptotic big $O()$ complexity alone might not always be sufficient to assess the practicality of a system. The paper consequently proposes a refined routing complexity measure for small-world networks. Simulation results confirm that random selection of shortcuts can achieve ``practical'' systems. Then, given that Kleinberg proved that the distribution of shortcuts has a strong impact on the routing complexity, arises the question of leveraging this result to improve upon current gossip-based protocols. We show that it is possible to design gossip-based protocols providing a good approximation of Kleinberg-like small-world topologies. Along, are presented simulation results that demonstrate the relevance of the proposed approach

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

Trust-Aware Peer Sampling: Performance and Privacy Tradeoffs

International audienceThe ability to identify people that share one's own interests is one of the most interesting promises of the Web 2.0 driving user-centric applications such as recommendation systems or collaborative marketplaces. To be truly useful, however, information about other users also needs to be associated with some notion of trust. Consider a user wishing to sell a concert ticket. Not only must she find someone who is interested in the concert, but she must also make sure she can trust this person to pay for it. This paper addresses the need for trust in user-centric applications by propos- ing two novel distributed protocols that combine interest-based connections be- tween users with explicit links obtained from social networks à-la Facebook. Both protocols build trusted multi-hop paths between users in an explicit so- cial network supporting the creation of semantic overlays backed up by social trust. The first protocol, TAPS2 , extends our previous work on TAPS (Trust- Aware Peer Sampling), by improving the ability to locate trusted nodes. Yet, it remains vulnerable to attackers wishing to learn about trust values between ar- bitrary pairs of users. The second protocol, PTAPS (Private TAPS ), improves TAPS2 with provable privacy guarantees by preventing users from revealing their friendship links to users that are more than two hops away in the social network. In addition to proving this privacy property, we evaluate the per- formance of our protocols through event-based simulations, showing significant improvements over the state of the art

Gossiping Personalized Queries

International audienceThis paper presents P3Q, a fully decentralized gossip-based protocol to personalize query processing in social tagging systems. P3Q dynamically associates each user with social acquaintances sharing similar tagging behaviours. Queries are gossiped among such acquaintances, computed on the fly in a collaborative, yet partitioned manner, and results are iteratively refined and returned to the querier. Analytical and experimental evaluations convey the scalability of P3Q for top-k query processing. More specifically, we show that on a 10,000-user delicious trace, with little storage at each user, the queries are accurately computed within reasonable time and bandwidth consumption. We also report on the inherent ability of P3Q to cope with users updating profiles and departing