Online Social Networks Providers Research Articles

Pricing Social Visibility Service in Online Social Networks: Modeling and Algorithms

Online social networks (OSNs) such as YouTube, Instagram, Twitter, Facebook, etc., serve as important platforms for users to share their information or content to friends or followers. Oftentimes, users want to enhance their social visibility, as it can make their contents, i.e., opinions, videos, pictures, etc., attract attention from more users, which in turn may bring higher commercial benefits to them. Motivated by this, we propose a mechanism, where the OSN operator provides a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“social visibility boosting service”</i> to incentivize “transactions” between requesters (users who seek to enhance their social visibility via adding new “neighbors”) and suppliers (users who are willing to be added as a new “neighbor” of any requester when certain “rewards” is provided). We design a posted pricing scheme for the OSN provider to charge the requesters who use such boosting service and reward the suppliers who make contributions. The OSN operator keeps a fraction of the payment from requesters and distributes the remaining part to participating suppliers <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“fairly”</i> via a rewarding rule based on Shapley value. We consider two different objectives/problems of the OSN provider, i.e., to select the optimal prices and supplier set to maximize (1) the revenue or (2) the welfare increase, under the requesters' budget constraint on suppliers. We first show that the problems are not simpler than NP-hard. We then decomposed each problem into two sub-routines, where one focuses on selecting the optimal set of suppliers, and the other one focuses on selecting the optimal prices. We prove the hardness of each sub-routine, and eventually design a computationally efficient approximation algorithm to solve the problems with provable theoretical guarantee on the revenue/welfare increase gap. We conduct extensive experiments on four public datasets to validate the performance of our proposed algorithms.

Image Compression Using Deep Learning: Methods and Techniques

In recent years images have been used widely by online social networks providers or numerous organizations such as governments, police departments, colleges, universities, and private companies. It held in vast databases. Thus, efficient storage of such images is advantageous and its compression is an appealing application. Image compression generally represents the significant image information compactly with a smaller size of bytes while insignificant image information (redundancy) already been removed for this reason image compression has an important role in data transfer and storage especially due to the data explosion that is increasing significantly. It is a challenging task since there are highly complex unknown correlations between the pixels. As a result, it is hard to find and recover a well-compressed representation for images, and it also hard to design and test networks that are able to recover images successfully in a lossless or lossy way. Several neural networks and deep learning methods have been used to compress images. This article survey most common techniques and methods of image compression focusing on auto-encoder of deep learning.

Analysis of influence contribution in social advertising

Online Social Network (OSN) providers usually conduct advertising campaigns by inserting social ads into promoted posts. Whenever a user engages in a promoted ad, she may further propagate the promoted ad to her followers recursively and the propagation process is known as the word-of-mouth effect. In order to spread the promotion cascade widely and efficiently, the OSN provider often tends to select the influencers, who normally have large audiences over the social network, to initiate the advertising campaign. This marketing model, also termed as influencer marketing, has been gaining increasing traction and investment and is rapidly becoming one of the most widely-used channels in digital marketing. In this paper, we formulate the problem for the OSN provider to derive the influence contributions of influencers given the campaign result, considering the viral propagation of the ads, namely influence contribution allocation (ICA) . We make a connection between ICA and the concept of Shapley value in cooperative game theory to reveal the rationale behind ICA. A naive method to obtain the solution to ICA is to enumerate all possible cascades delivering the campaign result, resulting in an exponential number of potential cascades, which is computationally intractable. Moreover, generating a cascade producing the exact campaign result is non-trivial. Facing the challenges, we develop an exact solution in linear time under the linear threshold (LT) model, and devise a fully polynomial-time randomized approximation scheme (FPRAS) under the independent cascade (IC) model. Specifically, under the IC model, we propose an efficient approach to estimate the expected influence contribution in probabilistic graphs modeling OSNs by designing a scalable sampling method with provable accuracy guarantees. We conduct extensive experiments and show that our algorithms yield solutions with remarkably higher quality over several baselines and improve the sampling efficiency significantly.

Verifiable Query Processing Over Outsourced Social Graph

Social data outsourcing is an emerging paradigm for effective and efficient access to the social data. In such a system, a third-party Social Data Provider (SDP) purchases social network datasets from Online Social Network (OSN) operators and then resells them to data consumers who can be any individuals or entities desiring social data through query interfaces. The SDP cannot be fully trusted and may return forged or incomplete query results to data consumers for various reasons, e.g., in favor of the businesses willing to pay. In this paper, we initiate the study on verifiable query processing over outsourced social graph whereby a data consumer can verify both the integrity and completeness of any query result returned by an untrusted SDP. We propose three schemes for single-attribute queries and another scheme for multi-attribute queries over outsourced social data. The four schemes all require the OSN provider to generate some cryptographic auxiliary information, based on which the SDP can construct a verification object to allow the data consumer to verify the integrity and completeness of the query result. They, however, differ in how the auxiliary information is generated and how the verification object is constructed and verified. Detailed analysis and extensive experiments using a real Twitter dataset confirm the efficacy and efficiency of the proposed schemes.

Interaction and Visualization Design for User Privacy Interface on Online Social Networks

The number of users participating in online social networks is increasing significantly recently. As a result, the amount of information created and shared by them is exploding. On the one hand, sharing information online helps people stay in touch with each other, although virtually. But on the other hand, sharing too much information may lead to sensitive personal data being leaked unexpectedly. To protect their users’ private information, online social network providers often employ technical methods like access control, cryptography, privacy preserving data processing, among others. Although these approaches are good enough for their designated purposes, they provide little to no protection when are used wrongly. To reduce the number of mistakes users may make, online social network providers also offer them visual interfaces, instead of lengthy and boring texts, for privacy settings selection and configuration. Unfortunately, private information is still shared publicly, with or without its owners’ awareness. In this paper, we attempt to mitigate the privacy leakage problem by proposing a novel visual model for measuring and representing users’ privacy in online social network environment and associated privacy controller for protecting it. A concrete instance of the model has been designed and implemented. A demonstration of the model instance has been executed for one of the biggest social networks, Facebook. Initial results indicate the effectiveness of the proposed model and its concrete instance. However, a more important and difficult problem is whether online social network providers are willing to apply these results, which may affect sharing activities and go against their business objectives.

Multi-Party Privacy Conflict Management in Online Social Networks: A Network Game Perspective

In this work, we consider the multi-party privacy conflict (MPC) in an online social network (OSN). As many data items uploaded to the OSN are “co-owned” by multiple users with different privacy concerns, some personal information of OSN users may be disclosed by others unintentionally. On the contrary with existing mainstream OSN platforms allowing only the very user uploading the data to set the privacy level, in this article we take a fine-grained approach to resolve MPC, in which all co-owners independently determine whether to share their personal content within the data on OSN. Interacted with its peers, the opinion of a co-owner, however, might be influenced by and consequently influence the decision of its peers. To this end, each co-owner, as an individual decision maker, strikes a tradeoff between its internal privacy preference and the external social influence from its neighbors in a OSN. Specifically, we formulate the interaction among co-owners as a multi-player non-cooperative game with a network structure representing their social relations. For the proposed network game, we establish the existence of multiple (pure-strategy) equilibria, and characterize them accordingly. The convergence of interaction is also investigated when synchronous and asynchronous best-response updates are used, respectively. We note that when the action set for the players is discrete, the game exhibits non-linear dynamics, making it challenging to analyze the convergence behavior. We prove that synchronous update may lead to either an equilibrium or a strategy cycle, and the asynchronous update always leads to an equilibrium. Building upon this analysis, we advocate a practical implementation of the proposed MPC management, which balances the automation of the management and intervention of users. Moreover, we take one step further to develop approaches aiming to reach a “stronger agreement” among the players for the sake of benefits of uploader and OSN provider. Numerical examples are also provided to corroborate the analytical results.

Privado

Online Social Networks (OSNs) offer free storage and social networking services through which users can communicate personal information with one another. The personal information of the users collected by the OSN provider comes with privacy problems when being monetized for advertising purposes. To protect user privacy, existing studies propose utilizing data encryption that immediately prevents OSNs from monetizing users data and hence leaves secure OSNs with no convincing commercial model. To address this problem, we propose Privado as a privacy-preserving group-based advertising mechanism to be integrated into secure OSNs to re-empower monetizing ability. Privado is run by N servers, each provided by an independent provider. User privacy is protected against an active malicious adversary controlling N − 1 providers, all the advertisers, and a large fraction of the users. We base our design on the group-based advertising notion to protect user privacy, which is not possible in the personalized variant. Our design also delivers advertising transparency; the procedure of identifying target customers is operated solely by the OSN servers without getting users and advertisers involved. We carry out experiments to examine the advertising running time under various number of servers and group sizes. We also argue about the optimum number of servers with respect to user privacy and advertising running time.

Pricing influential nodes in online social networks

Influential nodes with rich connections in online social networks (OSNs) are of great values to initiate marketing campaigns. However, the potential influence spread that can be generated by these influential nodes is hidden behind the structures of OSNs, which are often held by OSN providers and unavailable to advertisers for privacy concerns. A social advertising model known as influencer marketing is to have OSN providers offer and price candidate nodes for advertisers to purchase for seeding marketing campaigns. In this setting, a reasonable price profile for the candidate nodes should effectively reflect the expected influence gain they can bring in a marketing campaign. In this paper, we study the problem of pricing the influential nodes based on their expected influence spread to help advertisers select the initiators of marketing campaigns without the knowledge of OSN structures. We design a function characterizing the divergence between the price and the expected influence of the initiator sets. We formulate the problem to minimize the divergence and derive an optimal price profile. An advanced algorithm is developed to estimate the price profile with accuracy guarantees. Experiments with real OSN datasets show that our pricing algorithm can significantly outperform other baselines.

A Logical Key Hierarchy Based Approach to Preserve Content Privacy in Decentralized Online Social Networks

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 ( $n$ 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 $O(d\cdot log_d (n))$ O ( d · l o g d ( n ) ) encryption operations when a user is removed from a group (where $d$ d is an input parameter of the system), and $O(2\cdot log_d (n))$ O ( 2 · l o g d ( n ) ) when a user joins the group. The effectiveness of our approach is evaluated through simulations based on a real online social network.

Img-Shelter : Privacy Protection of Images In Online Social Networks Using Watermarking Scheme

Social networking sites are very useful in sharing information, making friends and keeping in touch with old friends. It is an online service, platform, or site that focuses on facilitating the building of social networks and social elation among peoples for sharing interests, activities, backgrounds, or real-life connections. But with the increasing demand of social networking sites (SNS) privacy and security concern have also increased. Protecting personal information privacy has become a controversial issue among online social network providers and users. Most social network providers have developed several techniques to decrease threats and risks to the users’ privacy. These risks include the misuse of personal information which may lead to illegal acts such as identity theft. This study aims to measure the awareness of users on protecting their personal information privacy, as well as the suitability of the privacy systems which they use to modify privacy settings. In this paper, categorize the picture as sensitive or normal. If it is sensitive means, perform copyrights algorithms. Then provide the permission to the receiver end for download the images in secure manner. Experimental result can be shows that in real time environments using C#.NET as front end and SQL SERVER as back end and comparative study of existing algorithms based on computational time and privacy rate.

Knowledge‐based approaches for identity management in online social networks

When we meet a new person, we start by introducing ourselves. We share our names, and other information about our jobs, cities, family status, and so on. This is how socializing and social interactions can start: we first need to identify each other. Identification is a cornerstone in establishing social contacts. We identify ourselves and others by a set of civil (e.g., name, nationality, ID number, gender) and social (e.g., music taste, hobbies, religion) characteristics. This seamlessly carried out identification process in face‐to‐face interactions is challenged in the virtual realms of socializing, such as in online social network (OSN) platforms. New identities (i.e., online profiles) could be created without being subject to any level of verification, making it easy to create fake information and forge fake identities. This has led to a massive proliferation of accounts that represent fake identities (i.e., not mapping to physically existing entities), and that poison the online socializing environment with fake information and malicious behavior (e.g., child abuse, information stealing). Within this milieu, users in OSNs are left unarmed against the challenging task of identifying the real person behind the screen. OSN providers and research bodies have dedicated considerable effort to the study of the behavior and features of fake OSN identities, trying to find ways to detect them. Some other research initiatives have explored possible techniques to enable identity validation in OSNs. Both kinds of approach rely on extracting knowledge from the OSN, and exploiting it to achieve identification management in their realms. We provide a review of the most prominent works in the literature. We define the problem, provide a taxonomy of related attacks, and discuss the available solutions and approaches for knowledge‐based identity management in OSNs.This article is categorized under: Fundamental Concepts of Data and Knowledge &gt; Human Centricity and User Interaction Application Areas&gt; Internet and Web‐Based Applications Application Areas&gt; Society and Culture

Optimizing cost for geo-distributed storage systems in online social networks

Globally distributed data centers provide an opportunity to deploy geo-distributed Online Social Networks (OSNs). For so big data generated by users, how to store them among those data centers is a key issue in the geo-distributed storage system. Today's popular OSN providers store users’ data in each deployed data center, so as to guarantee access latency. However, the full replication manner brings relatively high storage cost and traffic cost, which extremely increases the economic expenditure of OSN providers. Data placement based on social graph partitioning is an efficient way to minimize cost, but it requires the information of entire social graph and cannot fully guarantee latency. Recently, accomplished by partitioning replication is proposed to optimize cost as well as guarantee latency, but it has two drawbacks: (1) the separated manners of optimization cannot efficiently reduce the cost; (2) the placement of master replicas and slave replicas influence each other, and eventually reduces the optimization effects. In this paper, we explore an integrated manner of optimizing partitioning and replication simultaneously without distinguishing replica's role. We propose a lightweight replica placement (LRP) scheme, which conducts optimizations in a distributed manner and is well adapted to dynamic scenarios. Evaluations with two datasets from Twitter and Facebook show that LRP significantly reduces the cost compared with state-of-the-art schemes.

Towards Inferring Communication Patterns in Online Social Networks

The separation between the public and private spheres on online social networks is known to be, at best, blurred. On the one hand, previous studies have shown how it is possible to infer private attributes from publicly available data. On the other hand, no distinction exists between public and private data when we consider the ability of the online social network (OSN) provider to access them. Even when OSN users go to great lengths to protect their privacy, such as by using encryption or communication obfuscation, correlations between data may render these solutions useless. In this article, we study the relationship between private communication patterns and publicly available OSN data. Such a relationship informs both privacy-invasive inferences as well as OSN communication modelling, the latter being key toward developing effective obfuscation tools. We propose an inference model based on Bayesian analysis and evaluate, using a real social network dataset, how archetypal social graph features can lead to inferences about private communication. Our results indicate that both friendship graph and public traffic data may not be informative enough to enable these inferences, with time analysis having a non-negligible impact on their precision.

Multireceiver Predicate Encryption for Online Social Networks

Among the applications of the internet and cloud computing, online social network (OSN) is a very popular service. Since a lot of personal information is stored on the OSN platform, privacy protection on such an application has become a critical issue. Apart from this, OSN platforms need advertisement revenue to enable continued operations. However, if the users encrypt their messages, then OSN providers cannot generate accurate advertisement to users. Thus, how to achieve both privacy preserving and accurate advertisement is a worth-discussing issue. Unfortunately, none of the works on OSNs can achieve both privacy preserving and accurate advertisement simultaneously. In view of this, we propose the first multireceiver predicate encryption scheme for OSN platforms. Not only does the proposed scheme protects the users’ privacy but it achieves customized advertisement as well. Compared with other predicate encryptions deployed in OSN platforms, the proposed scheme gains much shorter ciphertext. The semantic security and attribute hiding of the proposed scheme are proved under the standard model.

A Study on Social Data Analytics and Privacy Concern among Social Media Users

Data Analytics represent a new era in the ability of organizations and businesses to tap the potential of the information economy. But this new opportunity comes with hazards and risks as well. Privacy is the major concern among social media users. Protecting their personal information privacy has become a controversial issue among online social network providers and users. This study aims to provide a literature study on social data analytics and privacy concern among social media users.

An Access Control Model for Online Social Networks Using User-to-User Relationships

Users and resources in online social networks (OSNs) are interconnected via various types of relationships. In particular, user-to-user relationships form the basis of the OSN structure, and play a significant role in specifying and enforcing access control. Individual users and the OSN provider should be enabled to specify which access can be granted in terms of existing relationships. In this paper, we propose a novel user-to-user relationship-based access control (UURAC) model for OSN systems that utilizes regular expression notation for such policy specification. Access control policies on users and resources are composed in terms of requested action, multiple relationship types, the starting point of the evaluation, and the number of hops on the path. We present two path checking algorithms to determine whether the required relationship path between users for a given access request exists. We validate the feasibility of our approach by implementing a prototype system and evaluating the performance of these two algorithms.

Optimizing Cost for Online Social Networks on Geo-Distributed Clouds

Geo-distributed clouds provide an intriguing platform to deploy online social network (OSN) services. To leverage the potential of clouds, a major concern of OSN providers is optimizing the monetary cost spent in using cloud resources while considering other important requirements, including providing satisfactory quality of service (QoS) and data availability to OSN users. In this paper, we study the problem of cost optimization for the dynamic OSN on multiple geo-distributed clouds over consecutive time periods while meeting predefined QoS and data availability requirements. We model the cost, the QoS, as well as the data availability of the OSN, formulate the problem, and design an algorithm named cosplay. We carry out extensive experiments with a large-scale real-world Twitter trace over 10 geo-distributed clouds all across the US. Our results show that, while always ensuring the QoS and the data availability as required, cosplay can reduce much more one-time cost than the state-of-the-art methods, and it can also significantly reduce the accumulative cost when continuously evaluated over 48 months, with OSN dynamics comparable to real-world cases.

Practical identity-based private sharing for online social networks

Online Social Networks (OSNs) constitute vital communication and information sharing channels. Unfortunately, existing coarse-grained privacy preferences insufficiently protect the shared information. Although cryptographic techniques provide interesting mechanisms to protect privacy, several issues remain problematic, such as, OSN provider acceptance, user adoption, key management and usability. To mitigate these problems, we propose a practical solution that uses Identity-Based Encryption to simplify key management and enforce data confidentiality. Moreover, we devise an Identity-Based outsider anonymous private sharing scheme to disseminate information among multiple users. Furthermore, we demonstrate the viability and tolerable overhead of our solution via an open-source prototype.

PESCA: a peer‐to‐peer social network architecture with privacy‐enabled social communication and data availability

The major challenge in current online social networks (OSNs) is privacy violation by OSN providers or unauthorised users. OSN providers collect unprecedented amounts of personal information for targeted advertising. Moreover, users are not able to share their social data with their friends with complete access control. Peer-to-peer (P2P) infrastructure is an interesting solution for a big-brother-free alternative to current OSN designs. However, the fundamental nature of P2P systems has dynamic peer turn-over which results in data unavailability. Additionally, users’ data must be available in the OSN when authorised data audiences want to access them. For these reasons, we propose a P2P-OSN architecture which is composed of a privacy enabled setup for users’ social communications and an adaptive replica placement strategy for ensuring availability for users’ shared data. The proposed framework correlates the availability of shared content in the P2P-OSN to the access control assigned to them. Our evaluations show the proposed P2P-OSN has considerable improvements in providing data privacy and availability compared with the existing approaches.

Handle with care: How online social network providers’ privacy policies impact users’ information sharing behavior

Privacy policies determine online social network providers’ options to monetize user data. However, these statements also intrude on users’ privacy and, thus, might reduce their willingness to disclose personal information, which in turn limits the data available for monetization. Given these conflicting interests, we conducted an experimental survey to investigate the relationship between privacy policies and users’ reactions. We show that users’ privacy risk perceptions mediate the effect that changes in policies’ monetization options have on users’ willingness to disclose information. Our findings emphasize privacy policies as a delicate managerial concept for companies relying on data monetization.