QUIC is a secure transport protocol that improves the performance of HTTPS. An initial QUIC handshake that enforces a strict validation of the client's source address requires two round-trips. In this work, we extend QUIC's address validation mechanism by an out-of-band validation token to save one round-trip time during the initial handshake. The proposed token allows sharing an address validation between the QUIC server and trusted entities issuing these tokens. This saves a round-trip time for the address validation. Furthermore, we propose distribution mechanisms for these tokens using DNS resolvers and QUIC connections to other hostnames. Our proposal can save up to 50 of the delay overhead of an initial QUIC handshake. Furthermore, our analytical results indicate that 363.6ms in total can be saved for all connections required to retrieve an average website, if a round-trip time of 90ms is assumed.
@cite_9 does not mitigate a stateless retry during the first connection to a member of a group of hostnames that have an exiting trust-relation with each other. Furthermore, these groups are usually rather small @cite_22 , thus only about 60 , validation tokens obtained from a member of such a group can be considered as trustworthy because these members share secret cryptographic state with each other such as a private key of a X.509 certificate.
@cite_9 @cite_22
2925272098 2912547735
Understanding the dynamics behind group formation and evolution in social networks is considered an instrumental milestone to better describe how individuals gather and form communities, how they enjoy and share the platform contents, how they are driven by their preferences tastes, and how their behaviors are influenced by peers. In this context, the notion of compactness of a social group is particularly relevant. While the literature usually refers to compactness as a measure to merely determine how much members of a group are similar among each other, we argue that the mutual trustworthiness between the members should be considered as an important factor in defining such a term. In fact, trust has profound effects on the dynamics of group formation and their evolution: individuals are more likely to join with and stay in a group if they can trust other group members. In this paper, we propose a quantitative measure of group compactness that takes into account both the similarity and the trustworthiness among users, and we present an algorithm to optimize such a measure. We provide empirical results, obtained from the real social networks EPINIONS and CIAO, that compare our notion of compactness versus the traditional notion of user similarity, clearly proving the advantages of our approach. In a mobile ad-hoc network (MANET) architecture, there is no pre-existing fixed network infrastructure, and a mobile node in this network sends data packets to a destination node directly or through its neighbor nodes. This situation is of potential security concern since the neighbor nodes cannot be always trusted. In this paper, we design a group member authentication protocol used in a MANET. It aims to allow a set of nodes to legitimately participate in group communication and then distribute a secret group key to the approved nodes to establish secure communication with group members. Our protocol provides knowledge-based group member authentication, which recognizes a list of secret group keys held in a mobile node as the node's group membership. It employs zero knowledge proof and threshold cryptography. We then introduce our actual implementation and evaluate the behavior to ensure its successful deployment QUIC is a performance-optimized secure transport protocol and a building block of the upcoming HTTP 3 standard. To protect against denial-of-service attacks, QUIC servers need to validate the IP addresses claimed by their clients. So far, the QUIC protocol conducts address validation for each hostname separately using validation tokens. In this work, we review this practice and introduce a new QUIC transport parameter to allow a shared address validation across hostnames. This parameter indicates to the client, that an issued validation token can be used to abbreviate the address validation when connecting to specific other hostnames. Based on trust-relations between real-world hostnames we evaluate the performance benefits of our proposal. Our results suggest that a shared address validation saves a round-trip time on almost 60 of the required handshakes to different hosts during the first loading of an average website. Assuming a typical transatlantic connection with a round-trip time of 90ms. We find that deploying our proposal reduces the delay overhead to establish all required connections for an average website by 142.2ms. In this paper, we consider lightweight decentralised algorithms for achieving consensus in distributed systems. Each member of a distributed group has a private value from a fixed set consisting of, say, two elements, and the goal is for all members to reach consensus on the majority value. We explore variants of the voter model applied to this problem. In the voter model, each node polls a randomly chosen group member and adopts its value. The process is repeated until consensus is reached. We generalise this so that each member polls a (deterministic or random) number of other group members and changes opinion only if a suitably defined super-majority has a different opinion. We show that this modification greatly speeds up the convergence of the algorithm, as well as substantially reducing the probability of it reaching consensus on the incorrect value. We measure media bias by estimating ideological scores for several major media outlets. To compute this, we count the times that a particular media outlet cites various think tanks and policy groups, and then compare this with the times that members of Congress cite the same groups. Our results show a strong liberal bias: all of the news outlets we examine, except Fox News' Special Report and the Washington Times, received scores to the left of the average member of Congress. Consistent with claims made by conservative critics, CBS Evening News and the New York Times received scores far to the left of center. The most centrist media outlets were PBS NewsHour, CNN's Newsnight, and ABC's Good Morning America; among print outlets, USA Today was closest to the center. All of our findings refer strictly to news content; that is, we exclude editorials, letters, and the like. "The editors in Los Angeles killed the story. They told Witcover that it didn't ‘come off’ and that it was an ‘opinion’ story.… The solution was simple, they told him. All he had to do was get other people to make the same points and draw the same conclusions and then write the article in their words" (emphasis in original). Timothy Crouse, Boys on the Bus [1973, p. 116]. We consider the problem of threshold secret sharing in groups with hierarchical structure. In such settings, the secret is shared among a group of participants that is partitioned into levels. The access structure is then determined by a sequence of threshold requirements: a subset of participants is authorized if it has at least k0 0 members from the highest level, as well as at least k1 > k0 members from the two highest levels and so forth. Such problems may occur in settings where the participants differ in their authority or level of confidence and the presence of higher level participants is imperative to allow the recovery of the common secret. Even though secret sharing in hierarchical groups has been studied extensively in the past, none of the existing solutions addresses the simple setting where, say, a bank transfer should be signed by three employees, at least one of whom must be a department manager. We present a perfect secret sharing scheme for this problem that, unlike most secret sharing schemes that are suitable for hierarchical structures, is ideal. As in Shamir's scheme, the secret is represented as the free coefficient of some polynomial. The novelty of our scheme is the usage of polynomial derivatives in order to generate lesser shares for participants of lower levels. Consequently, our scheme uses Birkhoff interpolation, i.e., the construction of a polynomial according to an unstructured set of point and derivative values. A substantial part of our discussion is dedicated to the question of how to assign identities to the participants from the underlying finite field so that the resulting Birkhoff interpolation problem will be well posed. In addition, we devise an ideal and efficient secret sharing scheme for the closely related hierarchical threshold access structures that were studied by Simmons and Brickell. Social groups play a crucial role in social media platforms because they form the basis for user participation and engagement. Groups are created explicitly by members of the community, but also form organically as members interact. Due to their importance, they have been studied widely (e.g., community detection, evolution, activity, etc.). One of the key questions for understanding how such groups evolve is whether there are different types of groups and how they differ. In Sociology, theories have been proposed to help explain how such groups form. In particular, the common identity and common bond theory states that people join groups based on identity (i.e., interest in the topics discussed) or bond attachment (i.e., social relationships). The theory has been applied qualitatively to small groups to classify them as either topical or social. We use the identity and bond theory to define a set of features to classify groups into those two categories. Using a dataset from Flickr, we extract user-defined groups and automatically-detected groups, obtained from a community detection algorithm. We discuss the process of manual labeling of groups into social or topical and present results of predicting the group label based on the defined features. We directly validate the predictions of the theory showing that the metrics are able to forecast the group type with high accuracy. In addition, we present a comparison between declared and detected groups along topicality and sociality dimensions. Understanding the group characteristics in MMORPGs is important in user behavior studies since people tend to gather together and form groups due to their inherent nature. In this paper, we analyze the group activities of users in Aion, one of the largest MMORPGs, based on the records of the activities of 94,497 users. In particular, we focus on (i) how social interactions within a group differ from the ones across groups, (ii) what makes a group rise, sustain, or fall, (iii) how group members join and leave a group, and (iv) what makes a group end. We first find that structural patterns of social interactions within a group are more likely to be close-knit and reciprocative than the ones across groups. We also observe that members in a rising group (i.e., the number of members increases) are more cohesive, and communicate with more evenly within the group than the ones in other groups. Our analysis further reveals that if a group is not cohesive, not actively communicating, or not evenly communicating among members, members of the group tend to leave. In a study of the perceptions of 332 members of genetic support groups with one or more of 101 different genetic disorders in the family, it was found that as a result of a genetic disorder 25 percent of the respondents or affected family members believed they were refused life insurance, 22 percent believed they were refused health insurance, and 13 percent believed they were denied or let go from a job. Fear of genetic discrimination resulted in 9 percent of respondents or family members refusing to be tested for genetic conditions, 18 percent not revealing genetic information to insurers, and 17 percent not revealing information to employers. The level of perceived discrimination points to the need for more information to determine the extent and scope of the problem. We present ReGroup, a novel end-user interactive machine learning system for helping people create custom, on demand groups in online social networks. As a person adds members to a group, ReGroup iteratively learns a probabilistic model of group membership specific to that group. ReGroup then uses its currently learned model to suggest additional members and group characteristics for filtering. Our evaluation shows that ReGroup is effective for helping people create large and varied groups, whereas traditional methods (searching by name or selecting from an alphabetical list) are better suited for small groups whose members can be easily recalled by name. By facilitating on demand group creation, ReGroup can enable in-context sharing and potentially encourage better online privacy practices. In addition, applying interactive machine learning to social network group creation introduces several challenges for designing effective end-user interaction with machine learning. We identify these challenges and discuss how we address them in ReGroup. The rich set of interactions between individuals in the society results in complex community structure, capturing highly connected circles of friends, fam- ilies, or professional cliques in a social network. Due to the frequent changes in the activity and communication patterns of individuals, the associated social and communication network is subject to constant evolution. The cohesive groups of people in such networks can grow by recruiting new members, or contract by loos- ing members; two (or more) groups may merge into a single community, while a large enough social group can split into several smaller ones; new communities are born and old ones may disappear. We discuss a new algorithm based on a clique percolation technique, that allows to investigate in detail the time dependence of communities on a large scale and as such, to uncover basic relationships of the sta- tistical features of community evolution. According to the results, the behaviour of smaller collaborative or friendship circles and larger communities, e.g., institutions show significant differences. Social groups containing only a few members persist longer on average when the fluctuations of the members is small. In contrast, we find that the condition for stability for large communities is continuous changes in their membership, allowing for the possibility that after some time practically all members are exchanged. Users often wish to participate in online groups anonymously, but misbehaving users may abuse this anonymity to disrupt the group's communication. Existing messaging protocols such as DC-nets leave groups vulnerable to denial-of-service and Sybil attacks, Mix-nets are difficult to protect against traffic analysis, and accountable voting protocols are unsuited to general anonymous messaging. We present the first general messaging protocol that offers provable anonymity with accountability for moderate-size groups, and efficiently handles unbalanced loads where few members wish to transmit in a given round. The N group members first cooperatively shuffle an N x N matrix of pseudorandom seeds, then use these seeds in N "pre-planned" DC-nets protocol runs. Each DC-nets run transmits the variable-length bulk data comprising one member's message, using the minimum number of bits required for anonymity under our attack model. The protocol preserves message integrity and one-to-one correspondence between members and messages, makes denial-of-service attacks by members traceable to the culprit, and efficiently handles large, unbalanced message loads. A working prototype demonstrates the protocol's practicality for anonymous messaging in groups of 40+ members. Security in collaborative peer groups is an active research topic. Most previous work focused on key management without addressing an important pre-requisite: admission control, i.e., how to securely admit a new member. This paper represents an initial attempt to sketch out an admission control framework suitable for different flavors of peer groups and match them with appropriate cryptographic techniques and protocols. Open problems and directions for future work are identified and discussed. Wi-Fi Direct (WFD) has become almost a standard for peer-to-peer networking on smart devices. Typically, WFD allows a user to search for and connect to other devices, which results in creating a group of devices that can exchange data. In certain applications like alert dissemination in disaster areas, it is required to form groups dynamically and to share data across the group boundaries. However, these capabilities are not offered by the current WFD implementations. In this paper, we propose EMC, a novel protocol that allows WFD devices to dynamically cluster themselves into groups and appropriately elect group owners. EMC also allows members of distinct groups to communicate. EMC is validated through implementation on Android devices and through the realization of a chat application. Environmental factors affecting shared spaces are typically designed to appeal to the broadest audiences they are expected to serve, ignoring the preferences of the people actually inhabiting the environment at any given time. Examples of such factors include the lighting, temperature, decor or music in the common areas of an office building. We have designed and deployed MUSICFX, a group preference arbitration system that allows the members of a fitness center to influence, but not directly control, the selection of music in a fitness center. We present a number of empirical results from our work with this intelligent environment: the results of a poll of fitness center members, a quantitative evaluation of the performance of a group preference arbitrator in a shared environment, and some interesting anecdotes about members’ experiences with the system. This study investigated the effect of a single work group deviant on other group members' perceptions of the deviant, and their perceptions of the cohesiveness of the group as a whole. Group members, particularly those high in perceived self-typicality, were expected to downgrade the deviant, and view groups containing a deviant as less cohesive. Undergraduate management students were placed in a simulated organizational context in which deviance was manipulated so that the participant's work group contained either a single negative deviant or no deviant. Results showed that the deviant colleague was judged less favorably than the normative colleague, particularly by those high in perceived self-typicality. Groups that contained a deviant were perceived as having lower levels of task cohesion, but ratings of social cohesion varied depending on perceivers' self-typicality. The findings suggest that as well as attracting negative evaluations, deviant group members can adversely affect group cohesion. Online communities in the form of message boards, listservs, and newsgroups continue to represent a considerable amount of the social activity on the Internet. Every year thousands of groups ourish while others decline into relative obscurity; likewise, millions of members join a new community every year, some of whom will come to manage or moderate the conversation while others simply sit by the sidelines and observe. These processes of group formation, growth, and dissolution are central in social science, and in an online venue they have ramifications for the design and development of community software In this paper we explore a large corpus of thriving online communities. These groups vary widely in size, moderation and privacy, and cover an equally diverse set of subject matter. We present a broad range of descriptive statistics of these groups. Using metadata from groups, members, and individual messages, we identify users who post and are replied-to frequently by multiple group members; we classify these high-engagement users based on the longevity of their engagements. We show that users who will go on to become long-lived, highly-engaged users experience significantly better treatment than other users from the moment they join the group, well before there is an opportunity for them to develop a long-standing relationship with members of the group We present a simple model explaining long-term heavy engagement as a combination of user-dependent and group-dependent factors. Using this model as an analytical tool, we show that properties of the user alone are sufficient to explain 95 of all memberships, but introducing a small amount of per-group information dramatically improves our ability to model users belonging to multiple groups. This paper extends the standard network centrality measures of degree, closeness and betweenness to apply to groups and classes as well as individuals. The group centrality measures will enable researchers to answer such questions as ‘how central is the engineering department in the informal influence network of this company?’ or ‘among middle managers in a given organization, which are more central, the men or the women?’ With these measures we can also solve the inverse problem: given the network of ties among organization members, how can we form a team that is maximally central? The measures are illustrated using two classic network data sets. We also formalize a measure of group centrality efficiency, which indicates the extent to which a group's centrality is principally due to a small subset of its members. We present PolyLens, a new collaborative filtering recommender system designed to recommend items for groups of users, rather than for individuals. A group recommender is more appropriate and useful for domains in which several people participate in a single activity, as is often the case with movies and restaurants. We present an analysis of the primary design issues for group recommenders, including questions about the nature of groups, the rights of group members, social value functions for groups, and interfaces for displaying group recommendations. We then report on our PolyLens prototype and the lessons we learned from usage logs and surveys from a nine-month trial that included 819 users We found that users not only valued group recommendations, but were willing to yield some privacy to get the benefits of group recommendations Users valued an extension to the group recommender system that enabled them to invite non-members to participate, via email Sociometric feedback visualizes social signals among group members to increase their awareness of their communication patterns. We deployed the Meeting Mediator, a real-time sociometric feedback system to groups participating in two rounds of a social dilemma task: in one round, all members were co-located and in the other round, the members were geographically distributed. Laboratory results show that the sociometric feedback successfully increases the speaking time and the frequency of turn transitions of groups that are initially distributed and later co-located, and also leads to a higher cooperation rate, increasing the overall earnings of these groups. In addition, the sociometric feedback helps groups have a more consistent pattern of behavior before and after a change in their geographic distribution. Therefore, the sociometric feedback influences the communication patterns of distributed groups and makes them more cooperative. Furthermore, the sociometric feedback helps groups sustain those patterns of communication even after a change in geographic distribution.