Uniswap is regarded as the leading decentralized exchange (DEX) platform, allowing users to trade (i.e., swap) one asset for another without intermediaries, thanks to blockchain technology. The platform consists of smart contracts holding asset reserves, called liquidity pools, and its widespread adoption constitutes an opportunity for assessing whether the promises of decentralization of DEXes are effectively fulfilled in practice. For this reason, in this paper we propose an analysis of the Uniswap ecosystem, focusing on the implementation of version 3 on the Ethereum blockchain. First, we study liquidity pools and their core operations, aiming to quantify the overall activity levels. Next, we focus on liquidity provision to assess whether users diversify their participation across multiple pools. We then analyze asset swapping activity, identifying the most active pools and the most frequently traded assets. Finally, we perform a complex network analysis of multi-hop swaps, namely trades routed through multiple pools. We represent them as a graph and study its topological properties. Results show that pool creations and activity have grown steadily, but many pools trigger few operations and remain active for at most two weeks. Also, trader activity concentrates in pools involving the Wrapped Ether asset and major stablecoins, while about 60% of liquidity providers hold only a single position, thus participating in only one pool. Finally, network analysis suggested a preferential attachment phenomenon, with a few pools handling most swaps and acting symmetrically as both entry and exit bridges.

Non-Fungible Tokens (NFTs) are promising additions to the blockchain universe. After Ethereum introduced the NFT concept in 2015, it quickly rose in popularity, triggering other public blockchains to support their own versions of these new types of token by extending their smart contract capabilities to enable minting of these new tokens in their ecosystems. Ethereum is the reference for NFT development, establishing the token standards widely used today. The popularity of NFTs in Ethereum created a rich application ecosystem that also revealed limitations in scalability and throughput for this network. An attempt to solve these issues resulted in the creation of Flow, a new NFT-centric blockchain that claims to solve the scalability issues that plagued Ethereum. This paper presents the alternative NFT architecture introduced by Flow and how it compares with Ethereum’s version.

No abstract available.

No abstract available.

Decentralized applications, the driving force behind the new Web3 paradigm, require continuous access to blockchain data. Their adoption, however, is hindered by the constantly increasing size of blockchains and the sequential scan nature of their read operations, which introduce a clear inefficiency bottleneck. Also, the growing amount of data recorded on the blockchain makes resource-constrained light nodes dependent on untrusted full nodes for fetching information, with a consequent need for query authentication protocols ensuring result integrity. Motivated by these reasons, in this paper we propose the skip index, an indexing data structure that allows users to quickly retrieve information simultaneously from multiple blocks of a blockchain. Our solution is also designed to be used as an authenticated data structure to guarantee the integrity of query results for light nodes. We discuss the theoretical properties of skip indices, propose efficient algorithms for their construction and querying, and detail their computational complexity. Finally, we assess the effectiveness of our proposal through an experimental evaluation on the Ethereum blockchain. As a reference use case, we focus on the popular CryptoKitties application and simulate a scenario where users seek to retrieve the events generated by the service. Our experimental results suggest that the use of skip indices offers a constant multiplicative speedup, thanks to search times that are at most logarithmic within a chosen search window. This allows to reduce the number of visited blocks by up to two orders of magnitude if compared to the naive sequential approach currently in use. • We propose the skip index, a data structure for efficient blockchain data retrieval. • The skip index provides guarantees about the integrity of query results. • We devise efficient algorithms to construct and query skip indices. • Compared to a sequential scan, skip indices offer a constant multiplicative speedup. • Skip indices experimentally provide a speedup of up to two orders of magnitude.

This article provides an overview of the first part of the ACM TWEB’s Special Issue on Advances in Social Media Technologies and Analysis. It highlights both research and practical applications in this field.

This article provides an overview of the second part of the ACM TWEB’s Special Issue on Advances in Social Media Technologies and Analysis. It highlights both research and practical applications in this field.

Non-Fungible Tokens (NFTs) are currently used in a large number of scenarios, from digital art to the metaverse, to trace the ownership of assets exchanged between users. However, most NFT defining standards, such as the widely adopted ERC 721 for the Ethereum protocol, have been designed with immutable assets only in mind. As such, they are not suitable for representing assets with features that may need to be updated during their lifetime. To overcome this issue, in the literature have been proposed new models that properly represent and protect mutable assets through NFTs, such as Non Fungible Mutable Tokens, NMTs. In this paper, we expanded the NMT model with a security support meant to protect assets’ features updates through access control policies that are defined by the asset creator and the current asset owner and enforced during the assets’ lifetime. Policing updates is of paramount importance, because it protects the asset from unintended updates that could greatly alter the asset itself and its value. The main contributions of this paper are a detailed description of the NMT smart contracts architecture and internal dependencies, as well as an experimental validation of NMTs by providing the implementation of a NMT representing a wearable (a jacket) in Decentraland, a popular metaverse environment.

Attribute-Based Access Control (ABAC) systems are commonly used for protecting resources from unauthorized accesses. They perform the access decision process taking into account a set of attributes describing the features of the subjects executing the accesses, the resources being accessed, and the environment. In this paper, we propose to enhance traditional ABAC systems by integrating them with the novel Self Sovereign Identity (SSI) paradigm. More precisely, we propose a novel access control framework, leveraging the well known eXtensible Access Control Markup Language (XACML) standard for ABAC systems, which allows to exploit SSI credentials as sources of users’ attributes for policy evaluation. Finally, we showcase a prototype implementation of our proposal, and we analyze its performance when evaluating XACML policies integrating SSI credentials with a variable number of attributes.

A blockchain is a data structure consisting of a list of blocks containing transactions and maintained by a network of nodes in a decentralized manner. In permissionless blockchains, anyone can contribute to the decentralization and security of the transactions. With the advent of smart contracts, programs whose execution is replicated by all the nodes of the network, the blockchain can be deemed not only a reliable and auditable data repository, but also a secure and verifiable computational infrastructure. However, due to the aforementioned features, the throughput of most permissionless blockchains is low, and executing a smart contract can be expensive, depending on its computational complexity. To mitigate these issues, a popular research line studies the implementation of Layer 2 solutions, which consists of nodes that operate off-chain yet remaining tethered to the blockchain. Our literature analysis revealed that a majority of the research articles surveying Layer 2 technologies and solutions typically classify them on the basis of the Layer 2 operations they perform, as well as their ability to improve the processing capacity of the blockchain. In this paper, instead, we survey the methodologies that provide a secure binding between Layer 2 and the blockchain. We refer to these binding techniques as “proving schemes” which we classify as: data integrity proofs, validity proofs, and fraud proofs. For each proving scheme, we describe its intended purpose, the advantages it offers, the methodologies commonly used to connect the operations performed at Layer 2 with the blockchain, and the applications that benefit from such scheme. Finally, we discuss and compare them to give a general comprehension about how schemes can satisfy general requirements common to most Decentralized Applications.

Online social networks (OSNs) revolutionized how people interact with each other, and nowadays, thanks to blockchain technology, new solutions are being considered, giving birth to blockchain online social media (BOSMs). BOSMs use the blockchain to redistribute with their users the wealth generated by the platform through a rewarding system, assigning better rewards to socially impactful users. Thus, these new systems are characterized by highly intertwined economical and social aspects and constitute a new scenario in the world of social networks. Many scenarios, economic and social alike, show a phenomenon known as “the rich-get-richer,” which states that the richest actors of a system tend to become richer over time. To the best of our knowledge, in the scenario of BOSMs, where users can acquire cryptocurrency through their social actions, this type of phenomenon was not yet studied. In this article, we propose a methodological framework composed of three hypotheses that can help study the rich-get-richer phenomenon through a set of measures and indices. In addition, we apply the proposed framework to the Steem case study, showing how unevenly wealth is distributed on its blockchain and comparing our results to other scenarios.

The Self-Sovereign Identity (SSI) is a novel paradigm aimed at giving back users sovereignty over their digital identities. Adopting the SSI approach prevents users to have a distinct identity for each service they use, instead, use a unique decentralised identity for all the services they need to access. However, to really benefit from the SSI advantages, an actual decentralised implementation is needed to fit the specific requirements and limits of decentralised architectures, such as blockchain. To this aim, this paper proposes Algorand Identity (AlgoID), a new SSI framework for the Algorand blockchain which differs from the already existing one, because it is fully blockchain based, i.e., it exploits Algorand itself for the storage of the data identity and as the registry location. The proposed framework has been completely implemented and validated through experiments, showing that the time required to execute the framework operations is acceptably low in realistic use cases.

In recent years, Decentralized Online Social Networks (DOSNs) have been attracting the attention of many users because they reduce the risk of censorship, surveillance, and information leakage from the service provider. In contrast to the most popular Online Social Networks, which are based on centralized architectures (e.g., Facebook, Twitter, or Instagram), DOSNs are not based on a single service provider acting as a central authority. Indeed, the contents that are published on DOSNs are stored on the devices made available by their users, which cooperate to execute the tasks needed to provide the service. A specific form of cooperation is to store the content published by a user on other peers’ devices as well, hence dramatically enhancing availability. Consequently, such contents must be properly protected by the DOSN infrastructure, in order to ensure that they can be really accessed only by users who have the permission of the publishers. As a consequence, DOSNs require efficient solutions for protecting the privacy of the contents published by each user with respect to the other users of the social network. This is exactly the focus of this paper. In particular, we investigate and compare the principal content privacy enforcement models adopted by current DOSNs evaluating their suitability to support different types of privacy policies based on user groups. Such evaluation is carried out by implementing several models and comparing their performance for the typical operations performed on groups, i.e., content publish, user join, and user leave. In detail, we show that the join operation incurs a similar cost for all the privacy enforcement models and groups, while for the leave operation performance is greatly affected by the selected solution, which must be evaluated on a case-by-case basis depending on both the type and the activity level of the group—as analytically detailed in our contribution. Further, we also highlight the limitations of current approaches and show future research directions. The provided contributions, other than being interesting on their own, set a blueprint for researchers and practitioners interested in implementing DOSNs, and highlight a few open research directions.

Recent years have witnessed, especially in Europe, a shift aimed at bringing users back at the center of digital systems. This has driven innovation towards the affirmation of decentralized systems, in line with the Self Sovereign Identity paradigm. User control over the consumption and disclosure of their data is a key topic of such drive. In this paper we show how it is possible to apply this increasingly popular concept to a traditionally centralized and opaque digital process: Access Control systems. To this aim we expand the XACML standard for Attribute Based Access Control systems with the novel concept of private attributes, i.e. attributes whose values should not be disclosed while still contributing to a policy evaluation result after user consent. Basing our proposal on blockchain systems, we show how to leverage smart contracts and zero knowledge proofs to allow for transparent policies evaluation without disclosing the value of such sensible attributes. Beside formalizing our goals, presenting the system architecture, and discussing its advantages and drawbacks with respect to the traditional model, we provide a reference example to show our proposal innovative capabilities and provide a prototype experimental evaluation to prove its feasibility.

The blockchain technology has been gaining an increasing popularity for the last years, and smart contracts are being used for a growing number of applications in several scenarios. The execution of smart contracts on public blockchains can be invoked by any user with a transaction, although in many scenarios there would be the need for restricting the right of executing smart contracts only to a restricted set of users. To help deal with this issue, this article proposes a system based on a popular access control framework called RT, Role-based Trust Management, to regulate smart contracts execution rights. The proposed system, called Layer 2 DecentrAlized Role-based Trust management (L2DART), implements the RT framework on a public blockchain, and it is designed as a layer-2 technology that involves both on-chain and off-chain functionalities to reduce the blockchain costs while keeping blockchain auditability, i.e., immutability and transparency. The on-chain costs of L2DART have been evaluated on Ethereum and compared with a previous solution implementing on-chain all the functionalities. The results show that the on-chain costs of L2DART are relatively low, making the system deployable in real-world scenarios.

Every person has associated a set of attributes that need to be shown to perform daily operations, such as the ones in personal identity card. The Self Sovereign Identity framework brings this to the digital world, giving to the user full responsibility of their own attributes. Each user receives a set of attributes is paired with as a Verifiable Credential, which could be issued, for example, by a municipality as a digitally signed ID card. However, it is not possible to disclose only one attribute of the ID card without invalidating the signature, therefore Selective Disclosure approaches have been defined to address this problem. This paper describes a selective disclosure method based on hashing, showing that the method is secure and applicable to the real world due to its low requirement in terms of space and time to create and verify a credential.

Abstract Many decentralised systems can be represented as graphs, and the detection of their community structure can uncover important properties. Several community detection algorithms have been proposed, however, only a few solutions are suitable for detecting and managing communities in a distributed and highly dynamic environment. This lacking is mainly due to the difficulty of defining self-organising solutions in the presence of a high rate of dynamism. The main contribution of this paper is DISCO, a distributed protocol for community detection and management in a Peer-to-Peer dynamic environment. Our approach is mainly targeted to Decentralised Online Social Networks (DOSNs), but it can be applied in other distributed scenarios. In the context of DOSNs, DISCO allows to discover communities in the local social network of a user, named ego network, and to manage their evolution over time. DISCO is based on a Temporal Trade-off approach and exploits a set of super-peers for the management of the communities. The paper presents an extensive evaluation of the proposed approach based on a dataset gathered from Facebook and shows the ability of DISCO to orchestrate a set of nodes to detect and manage communities in a highly dynamic and decentralised environment. The paper also proposes a comparison with a state of the art approach, showing that it is capable of reducing the number of critical community lifecycle events by over 25%, and reducing the average loading factor by up to 50%. Graphical abstract

The Interplanetary File System (IPFS) seeks to build a decentralized, fast and efficient file system able to connect all devices worldwide. In particular, its decentralized nature makes it viable to be applied to other decentralized applications, such as Decentralized Online Social Networks. Several Blockchain Online Social Networks adopted IPFS for storing larger resources, such as videos, letting them claim to be censorship free platforms. In this paper we inspect whether IPFS is a good choice as data storage for Decentralised Social Applications, discussing its strengths and weaknesses related to our scenario. We face the problem of data storage and persistence thanks to the so-called “pinning” services implemented on top of IPFS. Additionally, we provide a set of analyses concerning physical location, protocols, and identity of the IPFS nodes discovered by our crawling node.

The impact of Online Social Networks (OSNs) on the world has changed the way people interact with each other. During the last years, the trend to build virtual communities based on common interests, also called online social groups, has affirmed. Online social groups have been studied in the past, however, several aspects concerning the relationships between the group members are still unknown. Indeed, the definition of classical friendship relations in a social network is different from the social relationships established in a group. The former are defined with the will of the users, while the latter are implicitly activated by the interactions on the common topics. In this paper, we provide an analysis of the users' relationships present in 18 heterogeneous Facebook groups and we model the interactions of a group member within the group with the concept of member network. Then, we verify that the relationships present in the personal network of a user follow the Dunbar's property, previously detected in offline and online friendship ego networks. The results show that most personal networks present three or four circles as in classical Dunbar's structure, with a size comparable to the one initially described by Dunbar.

The Lightning Network is a P2P overlay that allows two Bitcoin users to create a new payment channel with a transaction, and later exchange cryptocurrency on that channel independently from the Bitcoin blockchain, improving Bitcoin’s scalability issues and making it more suitable for frequent micropayments. Moreover, the channels can also be used to route payments between two users who do not share a direct channel themselves.In this paper we study the topology of the Lightning Network in a given time period, performing a number of analysis on the channels and on the nodes composing the network. We study the churn rate, we evaluate centrality measurements and the clustering coefficient to evaluate the network connectivity, and finally we analyze the presence of a pattern that we named "bouquet". The importance of this pattern is proven by the finding that, removing specific nodes of the "bouquets" (1% of the total) causes the disconnection from the largest component of about 41% nodes.

In the past years, trust management systems have been proven suitable for solving the authorization problem in distributed systems, such as peer to peer systems, social networks, cloud, mobile ad-hoc networks, and Internet of things. Trust management systems could be either managed by a central authority or decentralized. In both cases the entity or, respectively, the set of entities managing the system need to be trusted for all users. To overcome this limitation, this paper brings blockchain technology into trust management systems, proposing a novel implementation of the Role-based Trust management framework (RT) on blockchain. The approach relies on smart contracts to represent user trust networks and to infer new trust relations through the chain discovery algorithm. We evaluated a prototype implemented on Ethereum on a representative set of policies related to different scenarios.

Recommender Systems (RSs) are becoming increasingly popular in the last years. They collect reviews concerning several types of items (e.g., shops, professionals, services, songs or videos) in order to rank them according to a given criterion, and to suggest the most relevant ones to their users. However, most of the currently used RSs exhibit two main drawbacks: they are based on a centralized control model and they do not provide reward mechanisms to encourage the participation of users. To deal with these challenges, the architectures of current RSs could be enhanced through blockchain technology, thus providing novel solutions to decentralize them. As a matter of fact, the blockchain technology could be successfully adopted in this context because smart contracts would allow the decentralization of system control, while cryptocurrency and tokens could be used to implement the reward mechanism. In the light of the above considerations, this manuscript presents a decentralized rating framework aimed to support the users of RSs based on blockchain technology, providing a token-based reward mechanism that remunerates users submitting their reviews to incentivize their participation. Moreover, the proposed system provides a flexible strategy to rank items, allowing users to choose among different functions to combine reviews to obtain item ranking. The performance and the cost of using the proposed system have been evaluated on the Ropsten Ethereum test network. For instance, our experiments have shown that the median time required to store a batch of 35 ratings is about 47 s, while the average time required to obtain the score of an item having 6000 ratings is less than 2.5 s.

Abstract In the last decades, temporal networks played a key role in modelling, understanding, and analysing the properties of dynamic systems where individuals and events vary in time. Of paramount importance is the representation and the analysis of Social Media, in particular Social Networks and Online Communities, through temporal networks, due to their intrinsic dynamism (social ties, online/offline status, users’ interactions, etc..). The identification of recurrent patterns in Online Communities, and in detail in Online Social Groups, is an important challenge which can reveal information concerning the structure of the social network, but also patterns of interactions, trending topics, and so on. Different works have already investigated the pattern detection in several scenarios by focusing mainly on identifying the occurrences of fixed and well known motifs (mostly, triads) or more flexible subgraphs. In this paper, we present the concept on the Incremental Communication Patterns, which is something in-between motifs, from which they inherit the meaningfulness of the identified structure, and subgraph, from which they inherit the possibility to be extended as needed. We formally define the Incremental Communication Patterns and exploit them to investigate the interaction patterns occurring in a real dataset consisting of 17 Online Social Groups taken from the list of Facebook groups. The results regarding our experimental analysis uncover interesting aspects of interactions patterns occurring in social groups and reveal that Incremental Communication Patterns are able to capture roles of the users within the groups.

Abstract Online Social Networking platforms (OSNs) have become part of people’s everyday life and their usage covers the deep-rooted need for communication among humans. During recent years, as people are questioning more and more OSN service providers, a new generation of proposals, based on blockchain became very popular thanks to the ethics adopted by these platforms. Steemit is the most important blockchain-based social networking site, which integrates, as main novelty an economic layer to the social media service. Steemit is implemented on top of Steem which, as in other blockchains, awards miners of the blocks with cryptocurrency. Steem miners, called witnesses, are not chosen based on the solution of a mathematical problem, as in Proof of Work based systems, but must be voted by other users. In this work, we decide to study the witnesses on Steem and their contribution to the social platform Steemit, and their social impact. We performed a set of analyses to shred light concerning their behaviour and to understand how they are socially perceived by other users. Analyses show an important social impact but, at the same time, some of them have a negative social impact. Their discussion is polarized towards content concerning Steem, Steemit, witnesses, and other platforms hosted on Steem.

Online social networks (OSNs) have changed the way of how people interact; however, lately, people are questioning more and more their business models. During the last ten years, new solutions based on decentralized architectures have been proposed, namely, decentralized OSNs (DOSNs) and blockchain online social medias (BOSMs). DOSNs were introduced several years ago and their main goal is the preservation of the privacy of the users in such a way that the data and the content of a user are always under their control. BOSMs leverage the usage of blockchain either to enforce the privacy of the users or to redistribute the wealth generated by the platform through a rewarding system. Steemit is the most stable and well-known BOSM with more than 1 million registered users, where users can create their own social network by following other users. To the best of our knowledge, no study exists on the relationship between the economic and social characteristics of BOSMs and on the way the rewarding system affects the social activity. The main goal of this article is to evaluate the characteristics of the Steemit follower-following graph to understand how the social and the economic aspects of BOSMs intertwine and influence each other. We study the properties of the Steemit follower-following graph and a few selected hotspot contents. The analysis shows that users are highly encouraged to be socially active, especially producing content, but the richest users are not also the most social ones, which suggests us that users can get rich without much involvement in the platform, using external mechanisms.

The widespread adoption of Online Social Networks (OSNs), the ever-increasing amount of information produced by their users, and the corresponding capacity to influence markets, politics, and society, have led both industrial and academic researchers to focus on how such systems could be influenced . While previous work has mainly focused on measuring current influential users, contents, or pages on the overall OSNs, the problem of predicting influencers in OSNs has remained relatively unexplored from a research perspective. Indeed, one of the main characteristics of OSNs is the ability of users to create different groups types, as well as to join groups defined by other users, in order to share information and opinions. In this article, we formulate the Influencers Prediction problem in the context of groups created in OSNs, and we define a general framework and an effective methodology to predict which users will be able to influence the behavior of the other ones in a future time period, based on historical interactions that occurred within the group. Our contribution, while rooted in solid rationale and established analytical tools, is also supported by an extensive experimental campaign. We investigate the accuracy of the predictions collecting data concerning the interactions among about 800,000 users from 18 Facebook groups belonging to different categories (i.e., News, Education, Sport, Entertainment, and Work). The achieved results show the quality and viability of our approach. For instance, we are able to predict, on average, for each group, around a third of what an ex-post analysis will show being the 10 most influential members of that group. While our contribution is interesting on its own and—to the best of our knowledge—unique, it is worth noticing that it also paves the way for further research in this field.

Distributed Online Social Networks (DOSNs) have been proposed to shift the control over user data from a unique entity, the online social network provider, to the users of the DOSN themselves. In this paper we focus on the problem of preserving the privacy of the contents shared to large groups of users. In general, content privacy is enforced by encrypting the content, having only authorized parties being able to decrypt it. When efficiency has to be taken into account, new solutions have to be devised that: i) minimize the re-encryption of the contents published in a group when the composition of the group changes; and, ii) enable a fast distribution of the cryptographic keys to all the members ($n$) of a group, each time a set of users is removed from or added to the group by the group owner. Current solutions fall short in meeting the above criteria, while our approach requires only $O(d cdot log_d(n))$ encryption operations when a user is removed from a group (where $d$ is an input parameter of the system), and $O(2cdot log_d(n))$ when a user joins the group. The effectiveness of our approach is evaluated through simulations based on a real online social network.

This work proposes to exploit blockchain technology to define Access Control systems that guarantee the auditability of access control policies evaluation. The key idea of our proposal is to codify attribute-based Access Control policies as smart contracts and deploy them on a blockchain, hence transforming the policy evaluation process into a completely distributed smart contract execution. Not only the policies, but also the attributes required for their evaluation are managed by smart contracts deployed on the blockchain. The auditability property derives from the immutability and transparency properties of blockchain technology. This paper not only presents the proposed Access Control system in general, but also its application to the innovative reference scenario where the resources to be protected are themselves smart contracts. To prove the feasibility of our approach, we present a reference implementation exploiting XACML policies and Solidity written smart contracts deployed on the Ethereum blockchain. Finally, we evaluate the system performances through a set of experimental results, and we discuss the advantages and drawbacks of our proposal.

With the rapid development and growth of online social networks (OSNs), researchers have been pushed forward to improve the knowledge of these complex networks by analyzing several aspects, such as the types of social media, the structural properties of the network, or the interaction patterns among users. In particular, a relevant effort has been devoted to the study and identification of cohesive groups of users in OSNs (also referred as communities) because they are the basic building block of each OSN. While several research works on groups in OSNs have mainly focused on identifying the types of groups and the contents created by their members, the analysis of internal organizations of such groups remains unexplored due to the lack of real data sets containing information about such groups, about their members, and the interactions among them. In this article, we compensate for this shortcoming by studying the main properties of groups defined in OSNs, taking as reference use cases 40 real Facebook groups of different categories that account for a total of about 500.000 users. In particular, we exploit interaction patterns among users and social network analysis to uncover interesting aspects related to the internal organization of groups. Experimental results reveal that the majority of the collected groups exhibit an internal structure where members can be clustered in four subgroups according to the level of tie strength of the relations they have. Furthermore, clusters identified on Facebook groups can provide relevant information about the importance of users within such groups.

Summary Avatars' mobility is an essential element to design, validate, and compare different distributed virtual environment architectures. It has a direct impact on the management of such systems because it defines the workload associated with the areas in the virtual world. Currently, a relevant part of this evaluation is conducted by means of synthetic traces generated through mobility models. Despite that, in the last decade, several models have been proposed in literature to describe avatars mobility. However, a standard methodology that drives researchers in their evaluation does not yet exist. In order to alleviate this issue, we presentTRACE, an open source tool supporting the generation and analysis of traces by means of embedded mobility models.TRACE's ultimate aim is to facilitate the evaluation and comparison of virtual environments and allow researchers to focus on developing their solution rather than spend time to code and test custom mobility traces.TRACEprovides a unified format to describe the traces. It enables scalable and efficient trace generation and analysis for thousands of avatars with seven built‐in models. Also, it defines APIs enabling the integration of additional models, different configurations of the environment, and several built‐in metrics to analyze the generated traces.

Large Scale

Opportunistic computing is a paradigm for completely self-organised pervasive networks. Instead of relying only on fixed infrastructures as the cloud, users’ devices act as service providers for each other. They use pairwise contacts to collect information about services provided and amount of time to provide them by the encountered nodes. At each node, upon generation of a service request, this information is used to choose the most efficient service, or composition of services, that satisfy that request, based on local knowledge. Opportunistic computing can be exploited in several scenarios, including mobile social networks, IoT, and Internet 4.0. In this paper, we propose an opportunistic computing algorithm based on an analytical model, which ranks the available (composition of) services, based on their expected completion time. Through the model, a service requester picks the one that is expected to be the best. Experiments show that the algorithm is accurate in ranking services, thus providing an effective service-selection policy. Such a policy achieves significantly lower service provisioning times compared to other reference policies. Its performance is tested in a wide range of scenarios varying the nodes mobility, the size of input/output parameters, the level of resource congestion, and the computational complexity of service executions.

Finding connected components is a fundamental task in applications dealing with graph analytics, such as social network analysis, web graph mining and image processing. The exponentially growing size of today's graphs has required the definition of new computational models and algorithms for their efficient processing on highly distributed architectures. In this paper we present CRACKER, an efficient iterative MapReduce-like algorithm to detect connected components in large graphs. The strategy of CRACKER is to transform the input graph in a set of trees, one for each connected component in the graph. Nodes are iteratively removed from the graph and added to the trees, reducing the amount of computation at each iteration. We prove the correctness of the algorithm, evaluate its computational cost and provide an extensive experimental evaluation considering a wide variety of synthetic and real-world graphs. The experimental results show that CRACKER consistently outperforms state-of-the-art approaches both in terms of total computation time and volume of messages exchanged.

Distributed Online Social Networks (DOSNs) have been recently proposed as an alternative to centralized solutions to allow a major control of the users over their own data. Since there is no centralized service provider which decides the term of service, the DOSNs infrastructure exploits users’ devices to take on the online social network services. In this paper, we propose a data allocation strategy for DOSNs which exploits the privacy policies of the users to increase the availability of the users’ contents without diverging from their privacy preferences. A set of replicas of the profile’s content of a user U are stored on the devices of other users who are entitled to access the profile according to U’s privacy policies. The experimental results obtained from the simulations on traces taken from a real social network show the effectiveness of our approach.

BITCOIN is a novel decentralized cryptocurrency system which has recently received a great attention from a wider audience. An interesting and unique feature of this system is that the complete list of all the transactions occurred from its inception is publicly available. This enables the investigation of funds movements to uncover interesting properties of the BITCOIN economy. In this paper we present a set of analyses of the user graph, i.e. the graph obtained by an heuristic clustering of the graph of BITCOIN transactions. Our analyses consider an up-to-date BITCOIN blockchain, as in December 2015, after the exponential explosion of the number of transactions occurred in the last two years. The set of analyses we defined includes, among others, the analysis of the time evolution of BITCOIN network, the verification of the "rich get richer" conjecture and the detection of the nodes which are critical for the network connectivity.

Statistical authorities promote and safeguard the production and publication of official statistics that serve the public good. One of their duties is to monitor the presence of individuals region by region. Traditionally this activity has been conducted by means of censuses and surveys. Nowadays technologies open new possibilities such as a continuous sensing of the presences by leveraging the data associated to mobile devices, e.g., the behaviour of users on doing calls. In this paper first we propose a specifically conceived similarity function able to capture similarity between individuals call behaviours. Second we make use of a clustering algorithm able to handle arbitrary metric leading to a good internal and external consistency of clusters. The approach provides better population estimation with respect to state of the art comparing with real census data. The scalability and flexibility that characterises the proposed framework enables novel scenarios for the characterization of people by means of data derived from mobile users, ranging from the nearly-realtime estimation of presences to the definition of complex, uncommon user archetypes.

Distributed Online Social Networks (DOSNs) have recently been proposed to shift the control over user data from a unique entity to the users of the DOSN themselves. In this paper, we focus our attention on the problem of privacy preserving content sharing to a large group of users of the DOSNs. Several solutions, based on cryptographic techniques, have been recently proposed. The main challenge here is the definition of a scalable and decentralized approach that: i) minimizes the re-encryption of the contents published in a group when the composition of the group changes and ii) enables a fast distribution of the cryptographic keys to all the members (n) of a group, each time a new user is added or removed from the group by the group owner. Our solution achieves the above goals, providing performance unattained by our competitors. In particular, our approach requires only O(d·logn) encryption operations when the group membership changes (eviction), and only O(2·logn) when a join occurs (where d is an input parameter of the system). The effectiveness of our approach is evaluated by an experimental campaign carried out over a set of traces from a real online social network.

We present NG-DBSCAN, an approximate density-based clustering algorithm that operates on arbitrary data and any symmetric distance measure. The distributed design of our algorithm makes it scalable to very large datasets; its approximate nature makes it fast, yet capable of producing high quality clustering results. We provide a detailed overview of the steps of NG-DBSCAN, together with their analysis. Our results, obtained through an extensive experimental campaign with real and synthetic data, substantiate our claims about NG-DBSCAN's performance and scalability.

Clustering items using textual features is an important problem with many applications, such as root-cause analysis of spam campaigns, as well as identifying common topics in social media. Due to the sheer size of such data, algorithmic scalability becomes a major concern. In this work, we present our approach for text clustering that builds an approximate k-NN graph, which is then used to compute connected components representing clusters. Our focus is to understand the scalability / accuracy tradeoff that underlies our method: we do so through an extensive experimental campaign, where we use real-life datasets, and show that even rough approximations of k-NN graphs are sufficient to identify valid clusters. Our method is scalable and can be easily tuned to meet requirements stemming from different application domains.

The problem of finding connected components in a graph is common to several applications dealing with graph analytics, such as social network analysis, web graph mining and image processing. The exponentially growing size of graphs requires the definition of appropriated computational models and algorithms for their processing on high throughput distributed architectures. In this paper we present cracker, an efficient iterative algorithm to detect connected components in large graphs. The strategy of cracker is to iteratively grow a spanning tree for each connected component of the graph. Nodes added to such trees are discarded from the computation in the subsequent iterations. We provide an extensive experimental evaluation considering a wide variety of synthetic and real-world graphs. The experimental evaluation shows that cracker consistently outperforms state-of-the-art approaches both in terms of total computation time and volume of messages exchanged.

Centralized Online Social Networks (OSNs) have become the main communication channel in both the personal and the business domain. A current trend for developing OSN services is towards the distribution of the social network infrastructure by using P2P architectures as basis for Distributed Online Social Networks (DOSNs). One of the main challenges of DOSNs comes from guaranteeing privacy and protection of private data. In previous work [18], we proposed a Dunbar-based approach to preserve data availability in DOSNs. Using Dunbar's circles of intimacy a certain level of trust is ensured which bases on the users confidence in their friends. Now, to achieve privacy and anonymity, we focus on the incorporation of social contacts into existing Peer-to-Peer Overlays and show that a naive integration of social links into existing Overlays like Chord and Pastry is not satisfactory. In order to address drawbacks of the naive approach we introduce goLLuM, a general solution which can be used on top of existing structured and unstructured P2P networks. Our protocol enables to route messages via friendly nodes only, even if only few friends per node exist. By using synthetic models and real-data traces for the representation of friendship relationships we highlight the drawbacks of the naive solution and show the functionality of goLLuM.

Summary A fundamental problem for the development of peer‐to‐peer (P2P) distributed virtual environments, like massively multiplayer on‐line games, is the definition of an overlay supporting interest management, that is, determining all the entities of the virtual world that are relevant for a given player. To this end, this paper proposes a gossip‐based approach that considers the coverage of the area of interest of peers as the guiding principle for the definition of the P2P overlay and its maintenance. The resulting overlay provides a support for a best‐effort resolution of interest management, mostly supported through communications on the P2P overlay, with minimal intervention of a centralized entity. The paper presents a set of extensive simulations based on realistic mobility traces. The experimental results show the effectiveness of gossiping for the construction and maintenance of a best‐effort overlay for interest management. Copyright © 2014 John Wiley & Sons, Ltd.

Summary This paper presents a novel Area Of Interest (AOI)‐cast algorithm for distributed virtual environments targeted to Delaunay‐based P2P overlays. The algorithm exploits the mathematical properties of Delaunay triangulations to build a spanning tree supporting the notification of the events generated by a peer to the other ones located in its AOI. The spanning tree is computed by thereversing compass routing, a routing algorithm proposed for geometric networks. Our approach presents a set of novel features. First, it requires only the knowledge of the peer's neighbors, so that the amount of traffic load on the P2P overlay is minimized. Second, we prove that, for circular shaped AOI, the algorithm builds a spanning tree covering all and only the peers of the AOI. Finally, our approach takes into account the possible inconsistencies among the local views of the peers, because the network latency, by introducing a tolerance threshold in the reverse compass routing. We present a set of simulations considering both synthetic data and real data traces taken from a real multiplayer game, which show the effectiveness of our proposal. Copyright © 2012 John Wiley & Sons, Ltd.

The increasing number and complexity of e-health services provided to citizens push healthcare organizations to adopt powerful computing systems to deal with the resulting workload. The adoption of the Cloud to host such services is a promising solution to address the increasing demand for computational power and also to deal with expected or unexpected computational peaks. This paper proposes a framework that allows the execution on the Cloud of the X1.V1 platform, which is a commercial product that supports a number of e-health services as well as the interoperability among different healthcare environments. This framework is meant to enable an easy and quick management of the Cloud resources allocated to the X1.V1 platform, in order to enhance the platform performances and optimize resources utilization. The design of the framework takes into account security aspects as well, because unauthorized accesses could lead to serious inefficiencies of the e-health services.

Opportunistic computing is a new computational paradigm enabling mobile users to access the heterogeneous services present in a pervasive mobile environment. With respect to conventional service-oriented approaches, in opportunistic computing services are provided by the users' mobile devices themselves, and are accessed exploiting opportunistically direct contacts between devices, i.e. without relying exclusively on fixed infrastructures such as the cloud. Pair-wise contacts are exploited to collect information on services and providers available in the network. A proper support may exploit this information to choose the most efficient composition of services satisfying a service request issued either by a user or an application. This paper defines a support for service selection and composition in opportunistic environments based on a mathematical model able to describe the different phases of the execution of a service composition. The model enables an estimation of the execution time of a composition and is exploited by the support for choosing the best composition among a set of available alternatives. The paper presents a set of simulations proving the effectiveness of our approach. The experiments show that our approach achieves better query resolution time and better load balancing of the service requests on the providers with respect to reference alternative approaches.

Over recent years, we experienced a huge diffusion of internet connected computing devices. As a consequence, this leaded to research for efficient and scalable approaches for managing the burden caused by the highly increased volume of data to be exchanged and processed. Efficient communication protocols are fundamental building blocks for realizing such approaches [1], [2]. Thus, several peer-to-peer protocols have been proposed. Gossip protocols [3]-[7] are a family of peer-to-peer protocols that proved to be well-suited for supporting a scalable and decentralized strategy for peer and data aggregation and diffusion. However, one of the typical limitation of Gossip protocols consists in the selfish behavior adopted by peers in defining their neighborhood and, as a consequence, the topology of the overlay they build. GROUP [8] is a Gossip protocol we conceived to overcome this limitation. It builds explicit defined communities of peers that are identified by their leaders, each one elected in a distributed fashion. This protocol experimentally proved to be efficient and effective with respect to its aim. Anyhow, no analytical study has been realized so far. This work presents a currently ongoing work we are conducting for exploring the properties of GROUP in a more formal way. We conduct this preliminary investigation using a formalization based on Markov chains.

In this paper we present DroidOppPathFinder, a Mobile Social Network application designed to generate and share contents about paths for fitness activity in a city. The application is able to recommend the best path in a specific area by analyzing the user's preferences and real-time environmental characteristics collected by heterogeneous sensing devices and services through opportunistic sensing mechanisms. To this aim, DroidOppPathFinder is developed on top of our middleware CAMEO, which provides context- and social-aware functionalities to improve both the application's performances and the user experience. This work represents a real example of opportunistic sensing service as additional support to the development of MSN applications. In addition, it demonstrates an efficient management of heterogeneous sensing data and services on mobile devices in order to further enrich the context of both local and remote nodes.

P2P overlays based on Delaunay triangulations have been recently exploited to implement systems providing efficient routing and data broadcast solutions. Several applications such as Distributed Virtual Environments and geographical nearest neighbours selection benefit from this approach. This paper presents a novel distributed algorithm for the incremental construction of a Delaunay overlay in a P2P network. The algorithm employs a distributed version of the classical Edge Flipping procedure. Each peer builds the Delaunay links incrementally by exploiting a random peer sample returned by the underlying gossip level. The algorithm is then optimized by considering the Euclidean distance between peers to speed up the overlay convergence. We present theoretical results that prove the correctness of our approach along with a set of experiments that assess the convergence rate of the distributed algorithm.

The problem of defining a support for multidimensional range queries on P2P overlays is currently an active field of research. Several approaches based on the extension of the basic functionalities offered by Distributed Hash Tables have been recently proposed. The main drawback of these approaches is that the locality required for the resolution of a range query cannot be guaranteed by uniform hashing. On the other way, locality preserving hashing functions do not guarantee a good level of load balancing. This paper presents Hivory, a P2P overlay based on a Voronoi tessellation defined by the objects published by peers. Each object is mapped to a site of the Voronoi tessellation and the corresponding Delaunay Triangulation defines the P2P overlay. A hierarchy of Voronoi diagrams is defined by exploiting clusters of objects paired with the same site of the Voronoi diagram. A new Voronoi diagram including the peers of the cluster is created so that the query resolution may be refined by a top down visit of the Voronoi hierarchy. The paper presents the proposed solution, analysis its complexity, and provides a set of experimental results.

This paper presents a P2P support for massively multiplayer on line games. The P2P overlay is defined by considering a Voronoi diagram where the sites correspond to the peers and a link connects Voronoi neighbors. An AOI-cast mechanism is defined to notify events generated by a peer to peers located in its area of interest. A routing algorithm exploiting the properties of the Voronoi graph has been defined as well. AOI-cast has been evaluated through a set of simulations developed by Peersim.

This paper defines and evaluates a hierarchical distributed data structure, distributed digest trie, supporting range queries in P2P systems. Providing efficient support for these queries is currently a challenging research issue in the P2P field, as classical approaches based on distributed hash ta

Distributed Virtual Environments (DVE) such as military or civil protection distributed simulations and massively multiplayer online games (MMOG), for instance World of Warcraft or Second Life, are currently gaining increasingly attention in the software market.

This paper describes the study conducted to design and evaluate a two-level on-line scheduler to dynamically schedule a stream of sequential and multi-threaded batch jobs on large scale grids, made up of interconnected clusters of heterogeneous machines. The scheduler aims to schedule arriving jobs respecting their computational and deadline requirements, and optimizing the utilization of hardware resources as well as software resources.

This paper presents VoRaQue, a software layer supporting range queries on Voronoi P2P overlays. VoRaQue maps data in a 2-dimensional space. The P2P overlay is defined by links connecting nodes that are close in the 2-dimensional space and by a set of long-range links which guarantee a poly-logarithmic routing. When a query is submitted, VoRaQue finds out a node belonging to the region defined by the query. A multicast spanning tree covering that region is then built by applying compass routing, a distributed protocol to embed a spanning tree into a Delaunay Triangulation. The paper presents the basic VoRaQue protocol, then introduces a set of optimizations and finally presents some experimental results.

This paper introduces JaDE, a P2P support for the development of distributed virtual environments that improves DVE scalability through the notion of area of interest. JaDE defines a set of protocols to support both the active entities and passive objects of the DVE. The state of passive objects is replicated on a set of peers to increase the reliability and the responsiveness of the application. Since passive objects may be concurrently updated by the active entities of the DVE, a novel consistency protocol is defined together with a set of mechanisms to guarantee the persistence of passive objects in a DVE environment. The paper presents an implementation of JaDE which exploits the JXTA distributed platform and shows how the JaDE functionalities may be supported by JXTA protocols. A set of preliminary experimental results are discussed.

This paper presents QLSE (QoS-constrained list scheduling heuristics), a quality of service-based launch time scheduling algorithm for wide area grids. QLSE considers applications described by a task interaction graph (TIG) whose nodes and edges are labeled according to the Quality of Service requirements of the application. The high values obtained in the performance evaluation for both the tasks communication and computation throughput demonstrates the applicability of the proposed approach.

In this paper, we propose a new method, called Convergent Scheduling, for scheduling a continuous stream of batch jobs on the machines of large-scale computing farms. This method exploits a set of heuristics that guide the scheduler in making decisions. Each heuristics manages a specific problem constraint, and contributes to carry out a value that measures the degree of matching between a job and a machine. Scheduling choices are taken to meet the QoS requested by the submitted jobs, and optimizing the usage of hardware and software resources. We compared it with some of the most common job scheduling algorithms, i.e. Backfilling, and Earliest Deadline First. Convergent Scheduling is able to compute good assignments, while being a simple and modular algorithm.

This paper presents DiVES, a distributed support for the development of networked distributed virtual environments. DiVES exploits the publish subscribe interaction model to define a flexible communication support. An acyclic peer-to-peer network of brokers has been defined to support an event based communication framework. The network can be dynamically reconfigured and it can tolerate broker crashes by a proper recovery mechanism. A set of optimization strategies of the basic publish/subscribe routing mechanism has been defined through an accurate analysis of the information exchanged in DVE applications. The message traffic on the network is reduced by packing notifications and filters into a single message. Furthermore, approximated filters are introduced to further reduce message traffic. Advertisements are exploited to optimize the routing of filters.

This paper advocates virtualization technology as a methodology to solve the security problems that an organization has to face when contributes with its resources to a grid. In particular, this technology makes it possible to increase the overall security of any system by inserting a set of controls into the code that implements one virtual machine. In this way, a secure cooperation among virtual machine can be implemented. This generalizes the current approach that exploits virtualization only for the confinement of alternative programming environments resulting from the partitioning of a physical machine into a set of non-cooperating virtual machines. The ability to support cooperation among virtual machines may be used to define networks of cooperating virtual machines to execute distributed applications. The paper describes a general purpose approach to security based upon virtual networks of cooperating virtual machines and applies it to one of the most challenging problems: that of securing a grid environment

This paper presents Orespics, an environment to learn concurrency. Orespics supports a didactic methodology inspired by the constructionistic theory and based on the resolution of 'real life problems'. Students program microworlds using a language based on the integration of the Logo turtles commands along with a set of message passing constructs. The environment includes a window based interface and a set of tools to define the characteristics of the microworld, the behavior of the agents, and to monitor the execution of the program. The paper presents the programming language and shows the main functions of the environment using a concrete example.

A programming tool, called parallelizer, for the static optimization of concurrent programs is considered. The tool partitions the alternative command lists of a nondeterministic iterative command into distinct elements that are concurrently executed. To improve the program's performance, the tool determines a decomposition where the granularity of the resulting processes is close to optimal for the target parallel architecture. This requires that some parameters of the target architecture are taken into account. Search techniques traditionally used in artificial intelligence are exploited to determine an optimal alternative assignment. The implementation of the parallelizer is described and an example of its application is considered.>