Stochastic Privacy

Online services such as web search and e-commerce applications typically rely on the collection of data about users, including details of their activities on the web. Such personal data is used to enhance the quality of service via personalization of content and to maximize revenues via better targeting of advertisements and deeper engagement of users on sites. To date, service providers have largely followed the approach of either requiring or requesting consent for opting-in to share their data. Users may be willing to share private information in return for better quality of service or for incentives, or in return for assurances about the nature and extend of the logging of data. We introduce \emph{stochastic privacy}, a new approach to privacy centering on a simple concept: A guarantee is provided to users about the upper-bound on the probability that their personal data will be used. Such a probability, which we refer to as \emph{privacy risk}, can be assessed by users as a preference or communicated as a policy by a service provider. Service providers can work to personalize and to optimize revenues in accordance with preferences about privacy risk. We present procedures, proofs, and an overall system for maximizing the quality of services, while respecting bounds on allowable or communicated privacy risk. We demonstrate the methodology with a case study and evaluation of the procedures applied to web search personalization. We show how we can achieve near-optimal utility of accessing information with provable guarantees on the probability of sharing data

Estimating Firm-Level Demand at a Price Comparison Site: Accounting for Shoppers and the Number of Competitors

Clearinghouse models of online pricing---such as Varian (1980), Rosenthal (1980), Narasimhan (1988), and Baye-Morgan (2001)---view a price comparison site as an 'information clearinghouse' where shoppers and loyals obtain price and product information to make online purchases. These models predict that the responsiveness of a firm's demand to a change in its price depends on the number of sellers and whether the price change results in the firm charging the lowest price in the market. Using a unique firm-level dataset from Kelkoo.com (Yahoo!'s European price comparison site), we examine these predictions by providing estimates of the demand for PDAs. Our results indicate that the number of competing sellers and both the firm's location on the screen and relative ranking in the list of prices are important determinants of an online retailer's demand. We find that an online monopolist faces an elasticity of demand of about -2, while sellers competing against 10 other sellers face an elasticity of about -6. We also find empirical evidence of a discontinuous jump in a firm's demand as its price declines from the second-lowest to the lowest price. Our estimates suggest that about 13% of the consumers at Kelkoo are 'shoppers' who purchase from the seller offering the lowest price.Internet, Price Dispersion, Advertising