The explosive growth in unwanted email has prompted the development of techniques for the rejection of email, intended to shield recipients from the onerous task of identifying the legitimate email in their inboxes amid a sea of spam. Unfortunately, widely used contentbased filtering systems have converted the spam problem into a false positive one: email has become unreliable. Email acceptance techniques complement rejection ones; they can help prevent false positives by filing email into a user’s inbox before it is considered for rejection. Whitelisting, whereby recipients accept email from some set of authorized senders, is one such acceptance technique. We present Reliable Email (RE:), a new whitelisting system that incurs zero false positives among socially connected users. Unlike previous whitelisting systems, which require that whitelists be populated manually, RE: exploits friend-of-friend relationships among email correspondents to populate whitelists automatically. To do so, RE: permits an email’s recipient to discover whether other email users have whitelisted the email’s sender, while preserving the privacy of users ’ email contacts with cryptographic private matching techniques. Using real email traces from two sites, we demonstrate that RE: renders a significant fraction of received email reliable. Our evaluation also shows that RE: can prevent up to 88 % of the false positives incurred by a widely deployed email rejection system, at modest computational cost. 1

Can we use social networks to combat spam? This paper investigates the feasibility of MailRank, a new email ranking and classification scheme exploiting the social communication network created via email interactions. The underlying email network data is collected from the email contacts of all MailRank users and updated automatically based on their email activities to achieve an easy maintenance. MailRank is used to rate the sender address of arriving emails such that emails from trustworthy senders can be ranked and classified as spam or non-spam. The paper presents two variants: Basic MailRank computes a global reputation score for each email address, whereas in Personalized MailRank the score of each email address is different for each MailRank user. The evaluation shows that MailRank is highly resistant against spammer attacks, which obviously have to be considered right from the beginning in such an application scenario. MailRank also performs well even for rather sparse networks, i.e., where only a small set of peers actually take part in the ranking of email addresses.

We propose a novel collaborative filtering method for link spams on blogs. The key idea is to rely on manual identification of spams and share this information about spams through a network of trust. The blogger who has identified a spam tells a small number of fellow bloggers (content implantation) , and those who have not heard about it start a search using an adaptive percolation search, combined with content implantation, they contract the information about identified spam in only a fraction of the query period time without producing large volume of tra#c.

We introduce a collaborative anti-spam system that is based on pervasive global social email networks. Essentially, we provide a solution to this open research problem: given a network of N users who are willing to share information collaboratively (e.g. the digests or ngerprints of known spams), how do we search for each user's content e ciently and reliablyinadistributedmannerwithminimal tra c cost on the network? As a solution to this open problem, our proposed system employs the percolation search process, which makes the tra c generated due to queries for spamdigestsscalesublinearlyasafunctionof N. However, in order to reap the bene ts of this novel percolation search algorithm, the node degree distribution of the underlying network must be heavy-tailed. Interestingly, latent global social email networks comprising of personal contacts possess a power-law heavy-tailed degree distribution, which renders itself an ideal natural platform to employ the percolation search algorithm. As a result, our proposed distributed spam lter requires no dedicated peer-to-peer (P2P) systems or centralized server-based systems. We have performed large-scale simulations and we nd that the system achieves a spam detectionratecloseto100%, whilethefalsepositiverateiskeptaroundzero. Thebandwidth costperuseraswellasthesystem-widebandwidth cost are shown to be very low.

Summary. The structure of networks can be characterized by the frequency of different subnetwork patterns found within them. Where these frequencies deviate from what would be expected in random networks they are termed “motifs ” of the network. Interestingly, it is often found that, networks performing similar functions evidence similar motif frequencies. We present results from a motif analysis of networks produced by peer-to-peer protocols that support cooperation between selfish nodes. We were surprised to find that their motif profiles match closely protein structure networks. It is currently an open issue as to why this is. 1

Peer-to-peer applications can benefit from human friendship networks (e.g., e-mail contacts or instant message buddy lists). However these are not always available. We propose an algorithm, called SLACER, that allows peer nodes to create their own friendship networks, through randomized interactions, producing an artificial social network (ASN) where nodes share high trust with their neighbors. 1.

By harvesting friendship networks from e-mail contacts or instant message "buddy lists" Peer-to-Peer (P2P) applications can improve performance in low trust environments such as the Internet. However, natural social networks are not always suitable, reliable or available. We propose an algorithm (SLACER) that allows peer nodes to create and manage their own friendship networks. We evaluate performance using a canonical test application, requiring cooperation between peers for socially optimal outcomes. The Artificial Social Networks (ASN) produced are connected, cooperative and robust - possessing many of the disable properties of human friendship networks such as trust between friends (directly linked peers) and short paths linking everyone via a chain of friends. In addition to new application possibilities, SLACER could supply ASN to P2P applications that currently depend on human social networks thus transforming them into fully autonomous, self-managing systems.

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Spam is still an open problem from the network operator’s perspective. The common state-of-the-art strategy to place filters against spam is at the recipient’s edge. Although this strategy largely solves the spam problem from the user’s perspective – false positives/negatives may still exist – it cannot prevent spam from traversing the Internet. Consequently, with nowadays around 200 billion spam/day, spam continues to consume large amounts of Internet bandwidth and provokes non-negligible financial loss to network operators. Therefore it becomes imperative to mitigate spam much earlier than at the recipient’s edge. This goal has been recently accomplished only partially by placing filters at the edge of a social circle within a social network. In this paper we introduce LENS, a novel spam protection system based on the anti-social networking paradigm, which further mitigates spam beyond social circles. The key idea of this paradigm in LENS is to let users select legitimate and authentic users, called Gatekeepers (GKs), from outside their social circle and within pre-defined social distances. Unless a GK vouches for the emails of potential senders from outside the social circle of a particular recipient, those e-mails are prevented from transmission. This way LENS drastically reduces the consumption of Internet bandwidth by spam to control messages only. To evaluate the scalability of LENS we use publicly available online social network (OSN) datasets and demonstrate that it is feasible to use GKs in the order of hundreds to provide reliable email delivery from millions of potential users. Using real email traces from large commercial and academic units, we demonstrate that LENS is very effective in accepting all inbound legitimate emails. 1.

Every communications medium can be abused for unwanted messages, e.g. email is dominated by spam messages, and Peer-to-Peer (P2P) file sharing systems have high proportions of invalid files. At the same time, the interest in Online Social Networks (OSNs) has grown. OSNs attempt to reduce unwanted messages by restricting communication to approved individuals. OSNs require centralized management, and are too restrictive, disallowing any-to-any communication. This paper introduces a novel, anonymous, economical approach to providing any-to-any communication, utilizing an underlying social network in both distributed and centralized settings that reduces spam. Another issue in distributed systems is users who do not contribute to the network (freeloaders). MessageReaper provides strong incentives for nodes not to send spam and not to freeload. MessageReaper requires only local knowledge and no automatic content analysis (and thus can be combined with existing spam blocking algorithms). It uses a simple greedy routing algorithm, trust structures, and an outcome propagation phase to drastically reduce the amount of spam and penalize freeloading nodes–suppressing these at the source. The outcome of each interaction is identified by the end-users, and only the majority of these need to be correct. Message-Reaper achieves fast stabilization, incentives to neither send spam nor freeload, and achieves low false negatives (22%) and neglible false positives (< 1%) even with a large fraction of the network misbehaving (30%). This architecture is applicable to a variety of applications, from email to Instant Messaging (IM) to P2P file sharing to OSNs. 1.