Glimmers: Resolving the Privacy/Trust Quagmire

Many successful services rely on trustworthy contributions from users. To establish that trust, such services often require access to privacy-sensitive information from users, thus creating a conflict between privacy and trust. Although it is likely impractical to expect both absolute privacy and trustworthiness at the same time, we argue that the current state of things, where individual privacy is usually sacrificed at the altar of trustworthy services, can be improved with a pragmatic $Glimmer$ $of$ $Trust$, which allows services to validate user contributions in a trustworthy way without forfeiting user privacy. We describe how trustworthy hardware such as Intel's SGX can be used client-side -- in contrast to much recent work exploring SGX in cloud services -- to realize the Glimmer architecture, and demonstrate how this realization is able to resolve the tension between privacy and trust in a variety of cases

Systems And Methods For Detecting Call Provenance From Call Audio

Various embodiments of the invention are detection systems and methods for detecting call provenance based on call audio. An exemplary embodiment of the detection system can comprise a characterization unit, a labeling unit, and an identification unit. The characterization unit can extract various characteristics of networks through which a call traversed, based on call audio. The labeling unit can be trained on prior call data and can identify one or more codecs used to encode the call, based on the call audio. The identification unit can utilize the characteristics of traversed networks and the identified codecs, and based on this information, the identification unit can provide a provenance fingerprint for the call. Based on the call provenance fingerprint, the detection system can identify, verify, or provide forensic information about a call audio source.Georgia Tech Research Corporatio

Emotions in context: examining pervasive affective sensing systems, applications, and analyses

Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing

Rigor, Relevance, and Practical Significance: A Real-life Journey to Organizational Value

In this essay, we describe a research journey focusing on how to analyze mouse cursor movements, typing fidelity, and data from other human-computer interaction (HCI) devices to better understand the end-user online experience. We begin by defining organizational value and how it relates to other aspects that researchers use to assess academic research quality. We then describe and contrast our research journey by demonstrating key research milestones: from achieving statistical significance to achieving practical significance and, finally, to reaching relevance to practice. We then explain how we crossed the chasm between academic research and technology commercialization (i.e., the last research mile). We conclude by describing the process one can follow to develop an initial prototype—the minimal viable product (MVP)—and how demonstrations with potential customers provides continuous insight and validation for evolving the commercial product capabilities to meet constantly changing and evolving customer and industry needs

FraudDroid: Automated Ad Fraud Detection for Android Apps

Although mobile ad frauds have been widespread, state-of-the-art approaches in the literature have mainly focused on detecting the so-called static placement frauds, where only a single UI state is involved and can be identified based on static information such as the size or location of ad views. Other types of fraud exist that involve multiple UI states and are performed dynamically while users interact with the app. Such dynamic interaction frauds, although now widely spread in apps, have not yet been explored nor addressed in the literature. In this work, we investigate a wide range of mobile ad frauds to provide a comprehensive taxonomy to the research community. We then propose, FraudDroid, a novel hybrid approach to detect ad frauds in mobile Android apps. FraudDroid analyses apps dynamically to build UI state transition graphs and collects their associated runtime network traffics, which are then leveraged to check against a set of heuristic-based rules for identifying ad fraudulent behaviours. We show empirically that FraudDroid detects ad frauds with a high precision (93%) and recall (92%). Experimental results further show that FraudDroid is capable of detecting ad frauds across the spectrum of fraud types. By analysing 12,000 ad-supported Android apps, FraudDroid identified 335 cases of fraud associated with 20 ad networks that are further confirmed to be true positive results and are shared with our fellow researchers to promote advanced ad fraud detectionComment: 12 pages, 10 figure

Uniform: The Form Validation Language

Digital forms are becoming increasingly more prevalent but the ease of creation is not. Web Forms are difficult to produce and validate. This design project seeks to simplify this process. This project is comprised of two parts: a logical programming language (Uniform) and a web application. Uniform is a language that allows its users to define logical relationships between web elements and apply simple rules to individual inputs to both validate the form and manipulate its components depending on user input. Uniform provides an extra layer of abstraction to complex coding. The web app implements Uniform to provide business-level programmers with an interface to build and manage forms. Users will create form templates, manage form instances, and cooperatively complete forms through the web app. Uniform’s development is ongoing, it will receive continued support and is available as open-source. The web application is software owned and maintained by HP Inc. which will be developed further before going to market