Michael Benisch

When are users comfortable sharing locations with advertisers

As smartphones and other mobile computing devices have increased in ubiquity, advertisers have begun to realize a more effective way of targeting users and a promising area for revenue growth: location-based advertising. This trend brings to bear new questions about whether or not users will adopt products involving this potentially invasive form of advertising and what sorts of protections they should be given. Our real-world user study of 27 participants echoes earlier findings that users have significant privacy concerns regarding sharing their locations with advertisers. However, we examine these concerns in more detail and find that they are complex (e.g., relating not only to the quantity of ads, but the locations and times at which they are received). With advanced privacy settings, users stated they would feel more comfortable and share more information than with a simple opt-in/opt-out mechanism.

A comparative study of location-sharing privacy preferences in the United States and China

While prior studies have provided us with an initial understanding of people’s location-sharing privacy preferences, they have been limited to Western countries and have not investigated the impact of the granularity of location disclosures on people’s privacy preferences. We report findings of a 3-week comparative study collecting location traces and location-sharing preferences from two comparable groups in the United States and China. Results of the study shed further light on the complexity of people’s location-sharing privacy preferences and key attributes influencing willingness to disclose locations to others and to advertisers. While our findings reveal many similarities between US and Chinese participants, they also show interesting differences, such as differences in willingness to share location at “home” and at “work” and differences in the granularity of disclosures people feel comfortable with. We conclude with a discussion of implications for the design of location-sharing applications and location-based advertising.

Capturing social networking privacy preferences: can default policies help alleviate tradeoffs between expressiveness and user burden?

Social networking sites such as Facebook and MySpace thrive on the exchange of personal content such as pictures and activities. These sites are discovering that people’s privacy preferences are very rich and diverse. In theory, providing users with more expressive settings to specify their privacy policies would not only enable them to better articulate their preferences, but could also lead to greater user burden. In this article, we evaluate to what extent providing users with default policies can help alleviate some of this burden. Our research is conducted in the context of location-sharing applications, where users are expected to specify conditions under which they are willing to let others see their locations. We define canonical policies that attempt to abstract away userspecific elements such as a user’s default schedule, or canonical places, such as “work” and “home.” We learn a set of default policies from this data using decision-tree and clustering algorithms. We examine tradeoffs between the complexity / understandability of default policies made available to users, and the accuracy with which they capture the ground truth preferences of our user population. Specifically, we present results obtained using data collected from 30 users of location-enabled phones over a period of one week. They suggest that providing users with a small number of canonical default policies to choose from can help reduce user burden when it comes to customizing the rich privacy settings they seem to require.

Pricing for customers with probabilistic valuations as a continuous knapsack problem

In this paper, we examine the problem of choosing discriminatory prices for customers with probabilistic valuations and a seller with indistinguishable copies of a good. We show that under certain assumptions this problem can be reduced to the continuous knapsack problem (CKP). We present a new fast ε-optimal algorithm for solving CKP instances with asymmetric concave reward functions. We also show that our algorithm can be extended beyond the CKP setting to handle pricing problems with overlapping goods (e.g.goods with common components or common resource requirements), rather than indistinguishable goods.We provide a framework for learning distributions over customer valuations from historical data that are accurate and compatible with our CKP algorithm, and we validate our techniques with experiments on pricing instances derived from the Trading Agent Competition in Supply Chain Management (TAC SCM). Our results confirm that our algorithm converges to an ε-optimal solution more quickly in practice than an adaptation of a previously proposed greedy heuristic.

The impact of expressiveness on the effectiveness of privacy mechanisms for location-sharing

Abstract : A recent trend on the Web is a demand for higher levels of expressiveness in the mechanisms that mediate interactions such as the allocation of resources, matching of peers, or elicitation of opinions. In this paper, we demonstrate the need for greater expressiveness in privacy mechanisms, which control the conditions under which private information is shared on the Web. We begin by adapting our recent theoretical framework for characterizing expressiveness to this domain. By leveraging prior results, we are able to prove that any increase in allowed expressiveness for privacy mechanisms leads to a strict improvement in their efficiency (i.e., the ability of individuals to share information without violating their privacy constraints). We validate these theoretical results with a week-long human subject experiment, where we tracked the locations of 30 subjects. Each day we collected their stated ground truth privacy preferences regarding sharing their locations with different groups of people. Our results confirm that 1) most subjects had relatively complex privacy preferences, and 2) that privacy mechanisms with higher levels of expressiveness are significantly more efficient in this domain.

Examining DCSP coordination tradeoffs

Distributed Constraint Satisfaction Problems (DCSPs) provide a model to capture a broad range of cooperative multiagent problem solving settings. Researchers have generally proposed two different sets of approaches for solving DCSPs, backtracking based approaches, such as Asynchronous Backtracking (ABT), and mediation based approaches, such as Asynchronous Partial Overlay (APO). These sets of approaches differ in the levels of coordination employed during conflict resolution. While the computational and communication complexity of the backtracking based approaches is well understood, the tradeoffs in complexity involved in moving toward mediation based approaches are not. In this paper we comprehensively reexamine the space of mediation based approaches for DCSP and fill gaps in existing frameworks with new strategies. We present different mediation session selection rules, including a rule that favors smaller mediation sessions, and different mediation strategies, including a decentralized hybrid strategy based on ABT. We present empirical results on solvable 3-coloring and random binary DCSP problems, that accurately capture the computational and communication tradeoffs between ABT and various mediation based approaches. Our results confirm that under some circumstances the newly presented strategies dominate previously proposed techniques.

CMieux: Adaptive strategies for competitive supply chain trading

Supply chains are a central element of todays global economy. Existing management practices consist primarily of static interactions between established partners. Global competition, shorter product life cycles and the emergence of Internet-mediated business solutions create an incentive for exploring more dynamic supply chain practices. The supply chain trading agent competition (TAC SCM) was designed to explore approaches to dynamic supply chain trading between automated software agents. TAC SCM pits trading agents developed by teams from around the world against one another. Each agent is responsible for running the procurement, planning and bidding operations of a PC assembly company, while competing with others for both customer orders and supplies under varying market conditions. This paper presents Carnegie Mellon Universitys 2005 TAC SCM entry, the CMieux supply chain trading agent. CMieux implements a novel approach for coordinating supply chain bidding, procurement and planning, with an emphasis on the ability to rapidly adapt to changing market conditions. We present empirical results based on 200 games involving agents entered by 25 different teams during what can be seen as the most competitive phase of the 2005 tournament. Not only did CMieux perform among the top five agents, it significantly outperformed these agents in procurement while matching their bidding performance. We also simulated 40 games against the best publicly available agent binaries. Our results show CMieux has significantly better average overall performance than any of these agents.

CMieux: adaptive strategies for competitive supply chain trading

Supply chains are a central element of todays global economy. Existing management practices consist primarily of static interactions between established partners. Global competition, shorter product life cycles and the emergence of Internet-mediated business solutions create an incentive for exploring more dynamic supply chain practices. The Supply Chain Trading Agent Competition (TAC SCM) was designed to explore approaches to dynamic supply chain trading. TAC SCM pits against one another trading agents developed by teams from around the world. Each agent is responsible for running the procurement, planning and bidding operations of a PC assembly company, while competing with others for both customer orders and supplies under varying market conditions. This paper presents Carnegie Mellon Universitys 2005 TAC SCM entry, the CMieux supply chain trading agent. CMieux implements a novel approach to coordinating supply chain bidding, procurement and planning, with an emphasis on the ability to rapidly adapt to changing market conditions. We present empirical results based on 200 games involving agents entered by 25 different teams during what can be seen as the most competitive phase of the 2005 tournament. Not only did CMieux perform among the top five agents, it significantly outperformed these agents in procurement while matching their bidding performance.

Algorithms for closed under rational behavior (CURB) sets

We provide a series of algorithms demonstrating that solutions according to the fundamental game-theoretic solution concept of closed under rational behavior (CURB) sets in two-player, normal-form games can be computed in polynomial time (we also discuss extensions to n-player games). First, we describe an algorithm that identifies all of a players best responses conditioned on the belief that the other player will play from within a given subset of its strategy space. This algorithm serves as a subroutine in a series of polynomial-time algorithms for finding all minimal CURB sets, one minimal CURB set, and the smallest minimal CURB set in a game. We then show that the complexity of finding a Nash equilibrium can be exponential only in the size of a games smallest CURB set. Related to this, we show that the smallest CURB set can be an arbitrarily small portion of the game, but it can also be arbitrarily larger than the supports of its only enclosed Nash equilibrium. We test our algorithms empirically and find that most commonly studied academic games tend to have either very large or very small minimal CURB sets.

Improving users' consistency when recalling location sharing preferences

This paper presents a study of the effect of one instance of contextual cues, trajectory reminders, on the recollection of location sharing preferences elicited using a retrospective protocol. Trajectory reminders are user interface elements that indicate for a particular location of a persons trail across a city the locations visited before and after. The results of the study show that reminding users where they have been before and after a specific visited location can elicit more consistent responses in terms of stated location sharing preferences for that location visit. This paper argues that trajectory reminders are useful when collecting preference data with retrospective protocols because they can improve the quality of the collected data.