To address privacy concerns over Online Social Networks (OSNs), we propose a distributed, peer-to-peer approach coupled with encryption. Extending the distributed approach by direct data exchange between user devices removes the strict connectivity requirements of web-based OSNs. In order to verify the feasibility of this approach, we designed a twotiered architecture and protocols that recreate the core features of OSNs in a decentralized way. This paper focuses on the description of the prototype built for the P2P infrastructure for social networks, as a first step without the encryption part, and shares early experiences from the prototype and insights gained since first outlining the challenges and possibilities of decentralized alternatives to OSNs. 1.

Privacy—the protection of information from unauthorized disclosure—is increasingly scarce on the Internet. The lack of privacy is particularly true for popular peer-to-peer data sharing applications such as BitTorrent where user behavior is easily monitored by third parties. Anonymizing overlays such as Tor and Freenet can improve user privacy, but only at a cost of substantially reduced performance. Most users are caught in the middle, unwilling to sacrifice either privacy or performance. In this paper, we explore a new design point in this tradeoff between privacy and performance. We describe the design and implementation of a new P2P data sharing protocol, called OneSwarm, that provides users much better privacy than BitTorrent and much better performance than Tor or Freenet. A key aspect of the One-Swarm design is that users have explicit configurable control over the amount of trust they place in peers and in the sharing model for their data: the same data can be shared publicly, anonymously, or with access control, with both trusted and untrusted peers. One-Swarm’s novel lookup and transfer techniques yield a median factor of 3.4 improvement in download times relative to Tor and a factor of 6.9 improvement relative to Freenet. OneSwarm is publicly available and has been downloaded by hundreds of thousands of users since its release.

Applications and services that take advantage of social data usually infer social relationships using information produced only within their own context. We propose to combine social information from multiple sources into a directed and weighted social multigraph in order to enable novel sociallyaware applications and services. We present GeoS, our early prototype of a geo-social data management service which implements a representative set of social inferences. We demonstrate GeoS ’ potential for social applications on a collection of social data that combines collocation information and Facebook friendship declarations from 100 students. Categories and Subject Descriptors E.1 [Data]: Data Structures—graphs and networks;

Abstract—The current Social Web is centralized. Large information silos store all the users ’ profiles, their social links and much of the other personal data. In return for the reliable service the users allow their data and activities to be data mined by the service providers, which in this way increase their advertising revenue. As the social applications are storing increasingly more data and attracting more users, many questions about privacy, data ownership and data portability arise. In this paper we are going to critically assess the current state of the Social Web, identify several novel research problems and outline the possible solution: friend-tofriend computing (F2F). F2F is a completely decentralized architecture in which two computers can communicate only if their owners know one another. Constraining the connections to friends-only solves many of the security problems of the peer-to-peer architectures. We argue that a reliable social application platform can be built using F2F as the substrate. The platform gives the users much more control over their data than the current Social Web and ensures the level of privacy and security not possible in any centralized architecture. Groups can easily build their own ad-hoc networks and collaborate without the need for any servers or third-party services. I.

Abstract. Recent Internet applications, such as online social networks and user-generated content sharing, produce an unprecedented amount of social information, which is further augmented by location or collocation data collected from mobile phones. Unfortunately, this wealth of social information is fragmented across many different proprietary applications. Combined, it could provide a more accurate representation of the social world, and it could enable a whole new set of socially-aware applications. We introduce Prometheus, a peer-to-peer service that collects and manages social information from multiple sources and implements a set of social inference functions while enforcing user-defined access control policies. Prometheus is socially-aware: it allows users to select peers that manage their social information based on social trust and exploits naturallyformed social groups for improved performance. We tested our Prometheus prototype on PlanetLab and built a mobile social application to test the performance of its social inference functions under real-time constraints. We showed that the social-based mapping of users onto peers improves the service response time and high service availability is achieved with low overhead.

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Abstract—Maintaining a given level of data redundancy is a fundamental requirement of peer-to-peer (P2P) storage systems—to ensure desired data availability, additional replicas must be created when peers fail. Since the majority of failures in P2P networks are transient (i.e., peers return with data intact), an intelligent system can reduce significant replication costs by not replicating data following transient failures. Reliably distinguishing permanent and transient failures, however, is a challenging task, because peers are unresponsive to probes in both cases. In this paper, we propose Protector, an algorithm that enables efficient replication policies by estimating the number of “remaining replicas ” for each object, including those temporarily unavailable due to transient failures. Protector dramatically improves detection accuracy by exploiting two opportunities. First, it leverages failure patterns to predict the likelihood that a peer (and the data it hosts) has permanently failed given its current downtime. Second, it detects replication level across groups of replicas (or fragments), thereby balancing false positives for some peers against false negatives for others. Extensive simulations based on both synthetic and real traces show that Protector closely approximates the performance of a perfect “oracle” failure detector, and significantly outperforms time-out-based detectors using a wide range of parameters. Finally, we design, implement and deploy an efficient P2P storage system called AmazingStore by combining Protector with structured P2P overlays. Our experience proves that Protector enables efficient long-term data maintenance in P2P storage systems. Index Terms—Failure detector, P2P storage, availability, replication management. Ç

Unwanted traffic mitigation can be broadly classified into two main approaches: a) centralized security infrastructures that rely on a limited number of trusted monitors to detect and report malicious traffic; and b) highly distributed systems that leverage the experiences of multiple nodes within distinct trust domains. The first approach offers limited threat coverage and slow response times. The second approach is not widely adopted, partly due to the lack of guarantees regarding the trustworthiness of nodes that comprise the system. Our proposal, FaceTrust, aims to achieve the trustworthiness of centralized security services and the wide coverage and responsiveness of large-scale collaborative threat mitigation. FaceTrust is a large-scale peer-to-peer system designed to rapidly propagate behavioral reports concerning Internet entities (e.g., hosts, email signatures etc.). A FaceTrust node builds trust for its peers by auditing their behavioral reports and by leveraging the social network of FaceTrust administrators. A FaceTrust node combines the confidence its peers have in their own reports and the trust it places on its peers to derive the likelihood that the entity is malicious (e.g. being a spam bot). The simulation-based evaluation of our approach indicates its potential under a real-world deployment: during a simulated spam campaign, FaceTrust nodes characterized 71 % of spam bot connections as such with confidence greater than 75%. 1

The recent increase in the volume of recorded social interactions has the potential to enable a large class of innovative social applications and services. The decentralized management of such social information as a social graph distributed on a user-contributed peer-to-peer network is appealing due to privacy concerns. This paper studies the vulnerability of such a peer-to-peer system to attacks staged by malicious users who try to manipulate the graph or by malicious peers who try to manipulate the mining of the social graph. We discuss the effects and limitations of such attacks and we show experimentally how the distribution of the social data onto peers affects the system’s resilience. CR-number [subcat-

Abstract—In this paper we investigate a new computing paradigm, called SocialCloud, in which computing nodes are governed by social ties driven from a bootstrapping trustpossessing social graph. We investigate how this paradigm differs from existing computing paradigms, such as grid computing and the conventional cloud computing paradigms. We show that incentives to adopt this paradigm are intuitive and natural, and security and trust guarantees provided by it are solid. We propose metrics for measuring the utility and advantage of this computing paradigm, and using real-world social graphs and structures of social traces; we investigate the potential of this paradigm for ordinary users. We study several design options and trade-offs, such as scheduling algorithms, centralization, and straggler handling, and show how they affect the utility of the paradigm. Interestingly, we conclude that whereas graphs known in the literature for high trust properties do not serve distributed trusted computing algorithms, such as Sybil defenses—for their weak algorithmic properties, such graphs are good candidates for our paradigm for their self-load-balancing features.