Unwillingness to pay for privacy: A field experiment

We measure willingness to pay for privacy in a field experiment. Participants were given the choice to buy a maximum of one DVD from one of two online stores. One store consistently required more sensitive personal data than the other, but otherwise the stores were identical. In one treatment, DVDs were one Euro cheaper at the store requesting more personal information, and almost all buyers chose the cheaper store. Surprisingly, in the second treatment when prices were identical, participants bought from both shops equally often. -- Wir messen die Zahlungsbereitschaft für Datenschutz in einem Feldexperiment. Die Teilnehmer konnten maximal eine DVD bei einem von zwei Online-Shops kaufen. Einer der beiden Läden verlangte immer mehr sensitive Daten als der andere, aber abgesehen davon waren die Läden gleich. Im ersten Treatment waren alle DVDs genau einen Euro günstiger bei dem Laden, der mehr sensitive Daten abfragte, und fast alle Käufer wählten diesen günstigeren Laden. In einem zweiten Treatment mit identischen Preisen bei beiden Läden kauften die Teilnehmer überraschenderweise bei beiden Läden gleich häufig.privacy,willingness to pay,field experiments

Unwillingness to Pay for Privacy: A Field Experiment

We measure willingness to pay for privacy in a field experiment. Participants were given the choice to buy a maximum of one DVD from one of two online stores. One store consistently required more sensitive personal data than the other, but otherwise the stores were identical. In one treatment, DVDs were one Euro cheaper at the store requesting more personal information, and almost all buyers chose the cheaper store. Surprisingly, in the second treatment when prices were identical, participants bought from both shops equally often.privacy, willingness to pay, field experiments

Unwillingness to Pay for Privacy: A Field Experiment

We measure willingness to pay for privacy in a field experiment. Participants were given the choice to buy a maximum of one DVD from one of two online stores. One store consistently required more sensitive personal data than the other, but otherwise the stores were identical. In one treatment, DVDs were one Euro cheaper at the store requesting more personal information, and almost all buyers chose the cheaper store. Surprisingly, in the second treatment when prices were identical, participants bought from both shops equally often.privacy, willingness to pay, field experiments

Execution Models for Choreographies and Cryptoprotocols

A choreography describes a transaction in which several principals interact. Since choreographies frequently describe business processes affecting substantial assets, we need a security infrastructure in order to implement them safely. As part of a line of work devoted to generating cryptoprotocols from choreographies, we focus here on the execution models suited to the two levels. We give a strand-style semantics for choreographies, and propose a special execution model in which choreography-level messages are faithfully delivered exactly once. We adapt this model to handle multiparty protocols in which some participants may be compromised. At level of cryptoprotocols, we use the standard Dolev-Yao execution model, with one alteration. Since many implementations use a "nonce cache" to discard multiply delivered messages, we provide a semantics for at-most-once delivery

A data sharing platform for earables research

Ear-worn wearable devices, or earables, are a rapidly emerging sensor platform, with unique opportunities to collect a wide variety of sensor data, and build systems with novel human-computer interaction components. At this point in the development of the field, with projects such as eSense putting hardware in researchers' hands but being limited in reach, the sharing of datasets collected by researchers with the wider community would bring a number of benefits. A central data sharing platform would enable wider participation in earables research and improve the quality of projects, as well as being a vehicle for better data quality and data protection practices. We discuss the considerations behind building such a platform, and propose an architecture that would achieve better privacy-utility trade-offs than many existing data sharing efforts

Unwillingness to pay for privacy: a field experiment

"We measure willingness to pay for privacy in a field experiment. Participants were given the choice to buy a maximum of one DVD from one of two online stores. One store consistently required more sensitive personal data than the other, but otherwise the stores were identical. In one treatment, DVDs were one Euro cheaper at the store requesting more personal information, and almost all buyers chose the cheaper store. Surprisingly, in the second treatment when prices were identical, participants bought from both shops equally often." (author's abstract)"Wir messen die Zahlungsbereitschaft für Datenschutz in einem Feldexperiment. Die Teilnehmer konnten maximal eine DVD bei einem von zwei Online-Shops kaufen. Einer der beiden Läden verlangte immer mehr sensitive Daten als der andere, aber abgesehen davon waren die Läden gleich. Im ersten Treatment waren alle DVDs genau einen Euro günstiger bei dem Laden, der mehr sensitive Daten abfragte, und fast alle Käufer wählten diesen günstigeren Laden. In einem zweiten Treatment mit identischen Preisen bei beiden Läden kauften die Teilnehmer überraschenderweise bei beiden Läden gleich häufig." (Autorenreferat

Effective use of mathematical equations in an online learning environment

Mathematical analysis is an essential tool in the practice of science, technology, engineering, and mathematics, and consequently it is important for students in these subjects to demonstrate effective use of mathematics. In this talk we are interested in supporting the use of mathematical equations in an online learning environment; in particular, we require methods of supporting both the entry and automated marking of mathematical equations, in order to support immediate personalised feedback to the learner

Quantifying Privacy Loss of Human Mobility Graph Topology

Abstract Human mobility is often represented as a mobility network, or graph, with nodes representing places of significance which an individual visits, such as their home, work, places of social amenity, etc., and edge weights corresponding to probability estimates of movements between these places. Previous research has shown that individuals can be identified by a small number of geolocated nodes in their mobility network, rendering mobility trace anonymization a hard task. In this paper we build on prior work and demonstrate that even when all location and timestamp information is removed from nodes, the graph topology of an individual mobility network itself is often uniquely identifying. Further, we observe that a mobility network is often unique, even when only a small number of the most popular nodes and edges are considered. We evaluate our approach using a large dataset of cell-tower location traces from 1 500 smartphone handsets with a mean duration of 430 days. We process the data to derive the top−N places visited by the device in the trace, and find that 93% of traces have a unique top−10 mobility network, and all traces are unique when considering top−15 mobility networks. Since mobility patterns, and therefore mobility networks for an individual, vary over time, we use graph kernel distance functions, to determine whether two mobility networks, taken at different points in time, represent the same individual. We then show that our distance metrics, while imperfect predictors, perform significantly better than a random strategy and therefore our approach represents a significant loss in privacy.</jats:p