Big Brother is Listening to You: Digital Eavesdropping in the Advertising Industry

In the Digital Age, information is more accessible than ever. Unfortunately, that accessibility has come at the expense of privacy. Now, more and more personal information is in the hands of corporations and governments, for uses not known to the average consumer. Although these entities have long been able to keep tabs on individuals, with the advent of virtual assistants and “always-listening” technologies, the ease by which a third party may extract information from a consumer has only increased. The stark reality is that lawmakers have left the American public behind. While other countries have enacted consumer privacy protections, the United States has no satisfactory legal framework in place to curb data collection by greedy businesses or to regulate how those companies may use and protect consumer data. This Article contemplates one use of that data: digital advertising. Inspired by stories of suspiciously well-targeted advertisements appearing on social media websites, this Article additionally questions whether companies have been honest about their collection of audio data. To address the potential harms consumers may suffer as a result of this deficient privacy protection, this Article proposes a framework wherein companies must acquire users\u27 consent and the government must ensure that businesses do not use consumer information for harmful purposes

Influence Analysis towards Big Social Data

Large scale social data from online social networks, instant messaging applications, and wearable devices have seen an exponential growth in a number of users and activities recently. The rapid proliferation of social data provides rich information and infinite possibilities for us to understand and analyze the complex inherent mechanism which governs the evolution of the new technology age. Influence, as a natural product of information diffusion (or propagation), which represents the change in an individual’s thoughts, attitudes, and behaviors resulting from interaction with others, is one of the fundamental processes in social worlds. Therefore, influence analysis occupies a very prominent place in social related data analysis, theory, model, and algorithms. In this dissertation, we study the influence analysis under the scenario of big social data. Firstly, we investigate the uncertainty of influence relationship among the social network. A novel sampling scheme is proposed which enables the development of an efficient algorithm to measure uncertainty. Considering the practicality of neighborhood relationship in real social data, a framework is introduced to transform the uncertain networks into deterministic weight networks where the weight on edges can be measured as Jaccard-like index. Secondly, focusing on the dynamic of social data, a practical framework is proposed by only probing partial communities to explore the real changes of a social network data. Our probing framework minimizes the possible difference between the observed topology and the actual network through several representative communities. We also propose an algorithm that takes full advantage of our divide-and-conquer strategy which reduces the computational overhead. Thirdly, if let the number of users who are influenced be the depth of propagation and the area covered by influenced users be the breadth, most of the research results are only focused on the influence depth instead of the influence breadth. Timeliness, acceptance ratio, and breadth are three important factors that significantly affect the result of influence maximization in reality, but they are neglected by researchers in most of time. To fill the gap, a novel algorithm that incorporates time delay for timeliness, opportunistic selection for acceptance ratio, and broad diffusion for influence breadth has been investigated. In our model, the breadth of influence is measured by the number of covered communities, and the tradeoff between depth and breadth of influence could be balanced by a specific parameter. Furthermore, the problem of privacy preserved influence maximization in both physical location network and online social network was addressed. We merge both the sensed location information collected from cyber-physical world and relationship information gathered from online social network into a unified framework with a comprehensive model. Then we propose the resolution for influence maximization problem with an efficient algorithm. At the same time, a privacy-preserving mechanism are proposed to protect the cyber physical location and link information from the application aspect. Last but not least, to address the challenge of large-scale data, we take the lead in designing an efficient influence maximization framework based on two new models which incorporate the dynamism of networks with consideration of time constraint during the influence spreading process in practice. All proposed problems and models of influence analysis have been empirically studied and verified by different, large-scale, real-world social data in this dissertation

Colocation aware content sharing in urban transport

People living in urban areas spend a considerable amount of time on public transport. During these periods, opportunities for inter-personal networking present themselves, as many of us now carry electronic devices equipped with Bluetooth or other wireless capabilities. Using these devices, individuals can share content (e.g., music, news or video clips) with fellow travellers that happen to be on the same train or bus. Transferring media takes time; in order to maximise the chances of successfully completing interesting downloads, users should identify neighbours that possess desirable content and who will travel with them for long-enough periods. In this thesis, a peer-to-peer content distribution system for wireless devices is proposed, grounded on three main contributions: (1) a technique to predict colocation durations (2) a mechanism to exclude poorly performing peers and (3) a library advertisement protocol. The prediction scheme works on the observation that people have a high degree of regularity in their movements. Ensuring that content is accurately described and delivered is a challenge in open networks, requiring the use of a trust framework, to avoid devices that do not behave appropriately. Content advertising methodologies are investigated, showing their effect on whether popular material or niche tastes are disseminated. We first validate our assumptions on synthetic and real datasets, particularly movement traces that are comparable to urban environments. We then illustrate real world operation using measurements from mobile devices running our system in the proposed environment. Finally, we demonstrate experimentally on these traces that our content sharing system significantly improves data communication efficiency, and file availability compared to naive approaches

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Towards efficacy and efficiency in sparse delay tolerant networks

The ubiquitous adoption of portable smart devices has enabled a new way of communication via Delay Tolerant Networks (DTNs), whereby messages are routed by the personal devices carried by ever-moving people. Although a DTN is a type of Mobile Ad Hoc Network (MANET), traditional MANET solutions are ill-equipped to accommodate message delivery in DTNs due to the dynamic and unpredictable nature of people\u27s movements and their spatio-temporal sparsity. More so, such DTNs are susceptible to catastrophic congestion and are inherently chaotic and arduous. This manuscript proposes approaches to handle message delivery in notably sparse DTNs. First, the ChitChat system [69] employs the social interests of individuals participating in a DTN to accurately model multi-hop relationships and to make opportunistic routing decisions for interest-annotated messages. Second, the ChitChat system is hybridized [70] to consider both social context and geographic information for learning the social semantics of locations so as to identify worthwhile routing opportunities to destinations and areas of interest. Network density analyses of five real-world datasets is conducted to identify sparse datasets on which to conduct simulations, finding that commonly-used datasets in past DTN research are notably dense and well connected, and suggests two rarely used datasets are appropriate for research into sparse DTNs. Finally, the Catora system is proposed to address congestive-driven degradation of service in DTNs by accomplishing two simultaneous tasks: (i) expedite the delivery of higher quality messages by uniquely ordering messages for transfer and delivery, and (ii) avoid congestion through strategic buffer management and message removal. Through dataset-driven simulations, these systems are found to outperform the state-of-the-art, with ChitChat facilitating delivery in sparse DTNs and Catora unencumbered by congestive conditions --Abstract, page iv

Privacy-preserving and fraud-resistant targeted advertising for mobile devices

Online Behavioural Advertising (OBA) enables Ad-Networks to capitalize on the popularity of digital Publishers in order to target users with contextaware promotional materials from Advertisers. OBA has been shown to be very effective at engaging consumers but at the same time presents severe privacy and security threats for both users and Advertisers. Users view OBA as intrusive and are therefore reluctant to share their private data with Ad-Networks. In many cases this results in the adoption of anti-tracking tools and ad-blockers which reduces the system's performance. Advertisers on their part are susceptible to financial fraud due to Ad-Reports that do not correspond to real consumer activity. Consequently, user privacy is further violated as Ad-Networks are provoked into collecting even more data in order to detect fictitious Ad-Reports. Researchers have mostly approached user privacy and fraud prevention as separate issues while ignoring how potential solutions to address one problem will effect the other. As a result, previously proposed privacy-preserving advertising systems are susceptible to fraud or fail to offer fine-grain targeting which makes them undesirable by Advertisers while systems that focus on fraud prevention, require the collection of private data which renders them as a threat for users. The aim of our research is to offer a comprehensive solution which addresses both problems without resulting in a conflict of interest between Advertisers and users. Our work specifically focuses on the preservation of privacy for mobile device users who represent the majority of consumers that are targeted by OBA. To accomplish the set goal, we contribute ADS+R (Advert Distribution System with Reporting) which is an innovative advertising system that supports the delivery of personalized adverts as well as the submission of verifiable Ad-Reports on mobile devices while still maintaining user privacy. Our approach adopts a decentralized architecture which connects mobile users and Advertisers over a hybrid opportunistic network without the need for an Ad-Network to operate as administrative authority. User privacy is preserved through the use of peer-to-peer connections (serving as proxy connections), Anonymous- download technologies and cryptography, while Advertiser fraud is prevented by means of a novel mechanism which we termed Behavioural Verification. Behavioural Verification combines client-side processing with a blockchaininspired construction which enables Advertisers to certify the integrity of Ad-Reports without exposing the identity of the submitting mobile users. In comparison to previously proposed systems, ADS+R provides both (1) user privacy and (2) advert fraud prevention while allowing for (3) a tunable trade-off between resource consumption and security, and (4) the statistical analysis and data mining of consumer behaviours