While most people claim to be very concerned about their privacy, they do not consistently take actions to protect it. Web retailers detail their information practices in their privacy policies, but most of the time this information remains invisible to consumers. This paper reports on research undertaken to determine whether a more prominent display of privacy information will cause consumers to incorporate privacy considerations into their online purchasing decisions. We designed an experiment in which a shopping search engine interface, Privacy Finder, clearly displays privacy policy information provided by retailers in a machinereadable format. Privacy Finder annotates search results with a "privacy icon" and a "privacy report." The privacy icon provides a privacy rating for the retailer on a five-point scale. The privacy report summarizes information contained in traditional privacy policies in a short, concise format. Our research shows that providing accessible privacy information reduces the information asymmetry gap between merchants and consumers. This reduction tends to lead consumers to purchase from online retailers who better protect their privacy. Additionally, our study indicates that once privacy information is made more salient, some consumers are willing to pay a premium to purchase from more privacy protective websites.
The rapid adoption of location tracking and mobile social networking technologies raises significant privacy challenges. Today our understanding of people's location sharing privacy preferences remains very limited, including how these preferences are impacted by the type of location tracking device or the nature of the locations visited. To address this gap, we deployed Locaccino, a mobile location sharing system, in a four week long field study, where we examined the behavior of study participants (n=28) who shared their location with their acquaintances (n = 373.) Our results show that users appear more comfortable sharing their presence at locations visited by a large and diverse set of people. Our study also indicates that people who visit a wider number of places tend to also be the subject of a greater number of requests for their locations. Over time these same people tend to also evolve more sophisticated privacy preferences, reflected by an increase in time-and location-based restrictions. We conclude by discussing the implications our findings.
Voice has become a widespread and commercially viable interaction mechanism with the introduction of voice assistants (VAs), such as Amazon’s Alexa, Apple’s Siri, Google Assistant, and Microsoft’s Cortana. Despite their prevalence, we do not have a detailed understanding of how these technologies are used in domestic spaces. To understand how people use VAs, we conducted interviews with 19 users, and analyzed the log files of 82 Amazon Alexa devices, totaling 193,665 commands, and 88 Google Home Devices, totaling 65,499 commands. In our analysis, we identified music, search, and IoT usage as the command categories most used by VA users. We explored how VAs are used in the home, investigated the role of VAs as scaffolding for Internet of Things device control, and characterized emergent issues of privacy for VA users. We conclude with implications for the design of VAs and for future research studies of VAs.
Abstract-With the rapid increase in cloud services collecting and using user data to offer personalized experiences, ensuring that these services comply with their privacy policies has become a business imperative for building user trust. However, most compliance efforts in industry today rely on manual review processes and audits designed to safeguard user data, and therefore are resource intensive and lack coverage. In this paper, we present our experience building and operating a system to automate privacy policy compliance checking in Bing. Central to the design of the system are (a) LEGALEASE-a language that allows specification of privacy policies that impose restrictions on how user data is handled; and (b) GROK-a data inventory for Map-Reduce-like big data systems that tracks how user data flows among programs. GROK maps code-level schema elements to datatypes in LEGALEASE, in essence, annotating existing programs with information flow types with minimal human input. Compliance checking is thus reduced to information flow analysis of big data systems. The system, bootstrapped by a small team, checks compliance daily of millions of lines of ever-changing source code written by several thousand developers.
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