Norman M. Sadeh

Understanding and capturing people's privacy policies in a mobile social networking application

A number of mobile applications have emerged that allow users to locate one another. However, people have expressed concerns about the privacy implications associated with this class of software, suggesting that broad adoption may only happen to the extent that these concerns are adequately addressed. In this article, we report on our work on PeopleFinder, an application that enables cell phone and laptop users to selectively share their locations with others (e.g. friends, family, and colleagues). The objective of our work has been to better understand people’s attitudes and behaviors towards privacy as they interact with such an application, and to explore technologies that empower users to more effectively and efficiently specify their privacy preferences (or “policies”). These technologies include user interfaces for specifying rules and auditing disclosures, as well as machine learning techniques to refine user policies based on their feedback. We present evaluations of these technologies in the context of one laboratory study and three field studies.

Distributed constrained heuristic search

A model of decentralized problem solving, called distributed constrained heuristic search (DCHS), that provides both structure and focus in individual agent search spaces to optimize decisions in the global space, is presented. The model achieves this by integrating decentralized constraint satisfaction and heuristic search. It is a formalism suitable for describing a large set of distributed artificial intelligence problems. The notion of textures that allow agents to operate in an asynchronous concurrent manner is introduced. The use of textures coupled with distributed asynchronous backjumping, a type of distributed dependency-directed backtracking that the authors have developed, enables agents to instantiate variables in such a way as to substantially reduce backtracking. The approach has been tested experimentally in the domain of decentralized job-shop scheduling. A formulation of distributed job-shop scheduling as a DCHS and experimental results are presented. >

A conundrum of permissions: installing applications on an android smartphone

Each time a user installs an application on their Android phone they are presented with a full screen of information describing what access they will be granting that application. This information is intended to help them make two choices: whether or not they trust that the application will not damage the security of their device and whether or not they are willing to share their information with the application, developer, and partners in question. We performed a series of semi-structured interviews in two cities to determine whether people read and understand these permissions screens, and to better understand how people perceive the implications of these decisions. We find that the permissions displays are generally viewed and read, but not understood by Android users. Alarmingly, we find that people are unaware of the security risks associated with mobile apps and believe that app marketplaces test and reject applications. In sum, users are not currently well prepared to make informed privacy and security decisions around installing applications.

Semantic web technologies to reconcile privacy and context awareness

Increasingly, application developers are looking for ways to provide users with higher levels of personalization that capture different elements of a users operating context, such as her location, the task that she is currently engaged in, who her colleagues are, etc. While there are many sources of contextual information, they tend to vary from one user to another and also over time. Different users may rely on different location tracking functionality provided by different cell phone operators; they may use different calendar systems, etc. In this article, we describe work on a Semantic e-Wallet aimed at supporting automated identification and access of personal resources, each represented as a Semantic Web Service. A key objective is to provide a Semantic Web environment for open access to a users contextual resources, thereby reducing the costs associated with the development and maintenance of context-aware applications. A second objective is, through Semantic Web technologies, to empower users to selectively control who has access to their contextual information and under which conditions. This work has been carried out in the context of my-Campus, a context-aware environment aimed at enhancing everyday campus life. Empirical results obtained on Carnegie Mellons campus are discussed.

Variable and value ordering heuristics for the job shop scheduling constraint satisfaction problem

Abstract Practical constraint satisfaction problems (CSPs) such as design of integrated circuits or scheduling generally entail large search spaces with hundreds or even thousands of variables, each with hundreds or thousands of possible values. Often, only a very tiny fraction of all these possible assignments participates in a satisfactory solution. This article discusses techniques that aim at reducing the effective size of the search space to be explored in order to find a satisfactory solution by judiciously selecting the order in which variables are instantiated and the sequence in which possible values are tried for each variable. In the CSP literature, these techniques are commonly referred to as variable and value ordering heuristics. Our investigation is conducted in the job shop scheduling domain. We show that, in contrast with problems studied earlier in the CSP literature, generic variable and value heuristics do not perform well in this domain. This is attributed to the difficulty of these heuristics to properly account for the tightness of constraints and/or the connectivity of the constraint graphs induced by job shop scheduling CSPs. A new probabilistic framework is introduced that better captures these key aspects of the job shop scheduling search space. Empirical results show that variable and value ordering heuristics derived within this probabilistic framework often yield significant improvements in search efficiency and significant reductions in the search time required to obtain a satisfactory solution. The research reported in this article was the first one, along with the work of Keng and Yun (1989), to use the CSP problem solving paradigm to solve job shop scheduling problems. The suite of benchmark problems it introduced has been used since then by a number of other researchers to evaluate alternative techniques for the job shop scheduling CSP. The article briefly reviews some of these more recent efforts and shows that our variable and value ordering heuristics remain quite competitive

Empirical models of privacy in location sharing

The rapid adoption of location tracking and mobile social networking technologies raises significant privacy challenges. Today our understanding of peoples 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.

The supply chain trading agent competition

Supply chain management deals with the planning and coordination of bidding, production, sourcing and procurement activities associated with one or more products. It is central to todays global economy, leading to trillions of dollars in annual transactions worldwide. With the emergence of electronic marketplaces, it is only natural to seek automated solutions that are capable of rapidly evaluating a large number of bidding, sourcing and procurement options. In this paper, we detail a game we have designed to promote the research and evaluation of such solutions under realistic conditions. The game requires agents to manage the assembly of PCs, while competing with one another both for customer orders and for key components. We discuss how the game captures the complexity, stochasticity and competitive nature inherent to supply chain environments. A Web-based multi-agent simulation platform developed for the game was implemented in 2003 and validated in the context of the first Supply Chain Management Trading Agent Competition (TAC-SCM). A total of 20 teams from around the world competed with one another. We review agent strategies developed by different teams and discuss the merits of competition-based research over more traditional research methodologies in this area.

Resource allocation in distributed factory scheduling

A distributed factory scheduling model that is based on the concept of constraint-directed search within a centralized framework is presented. A set of texture measures that quantify several characteristics of the problem space being searched has been developed into heuristics that direct the searches conducted by scheduling agents. The heuristics have been used to direct the searches of a centralized activity-based scheduler, achieving good schedule quality and minimizing the likelihood of backtracking. The model discussed uses textures to direct distributed scheduling, optimizing decisions in the global search space by allowing agents to focus and direct searches in their individual search spaces. Experimental results are presented and confirm the effectiveness of the approach.<<ETX>>

Your Location has been Shared 5,398 Times!: A Field Study on Mobile App Privacy Nudging

Smartphone users are often unaware of the data collected by apps running on their devices. We report on a study that evaluates the benefits of giving users an app permission manager and sending them nudges intended to raise their awareness of the data collected by their apps. Our study provides both qualitative and quantitative evidence that these approaches are complementary and can each play a significant role in empowering users to more effectively control their privacy. For instance, even after a week with access to the permission manager, participants benefited from nudges showing them how often some of their sensitive data was being accessed by apps, with 95% of participants reassessing their permissions, and 58% of them further restricting some of their permissions. We discuss how participants interacted both with the permission manager and the privacy nudges, analyze the effectiveness of both solutions, and derive some recommendations.

Applying case-based reasoning and multi-agent intelligent system to context-aware comparative shopping

Comparative shopping is a promising web service in the field of mobile commerce. This paper aims to propose a context-aware comparative shopping. Multi-agent intelligent architecture is adopted to implement the autonomous negotiation mechanism between buyers and sellers. To automatically estimate user preferences to determine the best purchase, case-based reasoning and negotiation mechanism are utilized. We developed a prototype system and experiment to show the possibility of the mechanism proposed in this paper. We found that our mechanism with multi-agents yields more pay-off, total sales, and wins than the system without those features.