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25
Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing
- in Proceedings of the 18th annual international conference on Mobile computing and networking (Mobicom 2012
, 2012
"... Mobile phone sensing is a new paradigm which takes advantage of the pervasive smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile phone sensing system, the platform recruits smartphone users to provide sensing service. Existing mobile phone sensing a ..."
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Mobile phone sensing is a new paradigm which takes advantage of the pervasive smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile phone sensing system, the platform recruits smartphone users to provide sensing service. Existing mobile phone sensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for mobile phone sensing. We consider two system models: the platform-centric model where the platform provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the platform-centric model, we design an incentive mechanism using a Stackelberg game, where the platform is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the platform is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.
A Market for Unbiased Private Data: Paying Individuals According to their Privacy Attitudes
"... Since there is, in principle, no reason why third parties should not pay individuals for the use of their data, we introduce a realistic market that would allow these payments to be made while taking into account the privacy attitude of the participants. And since it is usually important to use unbi ..."
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Cited by 13 (1 self)
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Since there is, in principle, no reason why third parties should not pay individuals for the use of their data, we introduce a realistic market that would allow these payments to be made while taking into account the privacy attitude of the participants. And since it is usually important to use unbiased samples to obtain credible statistical results, we examine the properties that such a market should have and suggest a mechanism that compensates those individuals that participate according to their risk attitudes. Equally important, we show that this mechanism also benefits buyers, as they pay less for the data than they would if they compensated all individuals with the same maximum fee that the most concerned ones expect. 1 1
A Theory of Pricing Private Data
"... Personal data has value to both its owner and to institutions who would like to analyze it. Privacy mechanisms protect the owner’s data while releasing to analysts noisy versions of aggregate query results. But such strict protections of individual’s data have not yet found wide use in practice. Ins ..."
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Personal data has value to both its owner and to institutions who would like to analyze it. Privacy mechanisms protect the owner’s data while releasing to analysts noisy versions of aggregate query results. But such strict protections of individual’s data have not yet found wide use in practice. Instead, Internet companies, for example, commonly provide free services in return for valuable sensitive information from users, which they exploit and sometimes sell to third parties. As the awareness of the value of the personal data increases, so has the drive to compensate the end user for her private information. The idea of monetizing private data can improve over the narrower view of hiding private data, since it empowers individuals to control their data through financial means. In this paper we propose a theoretical framework for assigning prices to noisy query answers, as a function of their accuracy, and for dividing the price amongst data owners who deserve compensation for their loss of privacy. Our framework adopts and extends key principles from both differential privacy and query pricing in data markets. We identify essential properties of the price function and micropayments, and characterize valid solutions.
R.: Your browsing behavior for a big mac: Economics of personal information online, http: //arxiv.org/abs/1112.6098
"... Most online services (Google, Facebook etc.) operate by providing a service to users for free, and in return they collect and monetize personal information (PI) of the users. This operational model is inherently economic, as the “good ” being traded and monetized is PI. This model is coming under in ..."
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Most online services (Google, Facebook etc.) operate by providing a service to users for free, and in return they collect and monetize personal information (PI) of the users. This operational model is inherently economic, as the “good ” being traded and monetized is PI. This model is coming under increased scrutiny as online services are moving to capture more PI of users, raising serious privacy concerns. However, little is known on how users valuate different types of PI while being online, as well as the perceptions of users with regards to exploitation of their PI by online service providers. In this paper, we study how users valuate different types of PI while being online, while capturing the context by relying on Experience Sampling. We were able to extract the monetary value that 168 participants put on different pieces of PI. We find that users value their PI related to their offline identities more (3 times) than their browsing behavior. Users also value information pertaining to financial transactions and social network interactions more than activities like search and shopping. We also found that while users are overwhelmingly in favor of exchanging their PI in return for improved online services, they are uncomfortable if these same providers monetize their PI.
Network analysis of third party tracking: user exposure to tracking cookies through search. Web Intelligence
, 2013
"... Abstract—Internet advertisers reach millions of customers through practices that real time tracking of users ’ online activities. The tracking is conducted by third party ad services engaged by the Web sites to facilitate marketing campaigns. Previous research has investigated tracking practices and ..."
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Abstract—Internet advertisers reach millions of customers through practices that real time tracking of users ’ online activities. The tracking is conducted by third party ad services engaged by the Web sites to facilitate marketing campaigns. Previous research has investigated tracking practices and tracking agencies associated with popular Web sites. Here we investigate the network properties of the third party referral structures that facilitate gathering of user information for the delivery of personalized ads. By considering third party domains associated with the top ten search results for a diverse set of queries, we arrived at the networks of third party domains in four search markets. We show a consistent network structure across markets, with a dominant connected component that, on average, includes 92.8 % of network vertices and 99.8 % of the connecting edges. There is 99.5 % chance that a user will become tracked by all top 10 trackers within 30 clicks on search results. Finally, the third party networks exhibit properties of the small world networks. This implies a high-level global and local efficiency in spreading the user information and delivering targeted ads. Keywords—browser cookies; surveillance; search; network propagation; search queries; trackers I.
Privacy analytics
- SIGCOMM Comput. Commun. Rev
"... People everywhere are generating ever-increasing amounts of data, often without being fully aware of who is recording what about them. For example, initiatives such as mandated smart metering, expected to be widely deployed in the UK in the next few years and already attempted in countries such as t ..."
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Cited by 4 (1 self)
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People everywhere are generating ever-increasing amounts of data, often without being fully aware of who is recording what about them. For example, initiatives such as mandated smart metering, expected to be widely deployed in the UK in the next few years and already attempted in countries such as the Netherlands, will generate vast quantities of detailed, personal data about huge segments of the population. Neither the impact nor the potential of this society-wide data gathering are well understood. Once data is gathered, it will be processed – and society is only now beginning to grapple with the consequences for privacy, both legal and ethical, of these actions, e.g., Brown et al. [4]. There is the potential for great harm through, e.g., invasion of privacy; but also the potential for great benefits by using this data to make more efficient use of resources, as well as releasing its vast economic potential [28]. In this editorial we briefly discuss work in this area, the challenges still faced, and some potential avenues for addressing them.
Characterizing Privacy Leakage of Public WiFi Networks for Users on Travel
"... Abstract—Deployment of public wireless access points (also known as public hotspots) and the prevalence of portable computing devices has made it more convenient for people on travel to access the Internet. On the other hand, it also generates large privacy concerns due to the open environment. Howe ..."
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Abstract—Deployment of public wireless access points (also known as public hotspots) and the prevalence of portable computing devices has made it more convenient for people on travel to access the Internet. On the other hand, it also generates large privacy concerns due to the open environment. However, most users are neglecting the privacy threats because currently there is no way for them to know to what extent their privacy is revealed. In this paper, we examine the privacy leakage in public hotspots from activities such as domain name querying, web browsing, search engine querying and online advertising. We discover that, from these activities multiple categories of user privacy can be leaked, such as identity privacy, location privacy, financial privacy, social privacy and personal privacy. We have collected real data from 20 airport datasets in four countries and discover that the privacy leakage can be up to 68%, which means two thirds of users on travel leak their private information while accessing the Internet at airports. Our results indicate that users are not fully aware of the privacy leakage they can encounter in the wireless environment, especially in public WiFi networks. This fact can urge network service providers and website designers to improve their service by developing better privacy preserving mechanisms. I.
Management
"... Advertisements are the de-facto currency of the Internet with many popular applications (e.g. Angry Birds) and online services (e.g., YouTube) relying on advertisement generated revenue. However, the current economic models and mechanisms for mobile advertising are fundamentally not sustainable and ..."
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Advertisements are the de-facto currency of the Internet with many popular applications (e.g. Angry Birds) and online services (e.g., YouTube) relying on advertisement generated revenue. However, the current economic models and mechanisms for mobile advertising are fundamentally not sustainable and far from ideal. In particular, as we show, applications which use mobile advertising are capable of using significant amounts of a mobile users ’ critical resources without being controlled or held accountable. This paper seeks to redress this situation by enabling advertisement supported applications to become significantly more “userfriendly”. To this end, we present the design and implementation of CAMEO, a new framework for mobile advertising that 1) employs intelligent and proactive retrieval of advertisements, using context prediction, to significantly reduce the bandwidth and energy overheads of advertising, and 2) provides a negotiation protocol and framework that empowers applications to subsidize their data traffic costs by “bartering” their advertisement rights for access bandwidth from mobile ISPs. Our evaluation, that uses real mobile advertising data collected from around the globe, demonstrates that CAMEO effectively reduces the resource consumption caused by mobile advertising.
Pricing Private Data
"... We consider a market where buyers can access unbiased samples of private data by appropriately compensating the individuals to whom the data corresponds (the sellers) according to their privacy attitudes. We show how bundling the buyers’ demand can decrease the price that buyers have to pay per data ..."
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We consider a market where buyers can access unbiased samples of private data by appropriately compensating the individuals to whom the data corresponds (the sellers) according to their privacy attitudes. We show how bundling the buyers’ demand can decrease the price that buyers have to pay per data point, while ensuring that sellers are willing to participate. Our approach leverages the inherently randomized nature of sampling, along with the risk-averse attitude of sellers. We take a prior-free approach and introduce mechanisms that incentivize each individual to truthfully report his preferences in terms of different payment schemes. We then show that our mechanisms provide optimal price guarantees in several settings.
An Economic Analysis of User-Privacy Options in Ad-Supported Services
"... Abstract. We analyze the value to e-commerce website operators of offering privacy options to users, e.g., of allowing users to opt out of ad targeting. In particular, we assume that site operators have some control over the cost that a privacy option imposes on users and ask when it is to their adv ..."
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Abstract. We analyze the value to e-commerce website operators of offering privacy options to users, e.g., of allowing users to opt out of ad targeting. In particular, we assume that site operators have some control over the cost that a privacy option imposes on users and ask when it is to their advantage to make such costs low. We consider both the case of a single site and the case of multiple sites that compete both for users who value privacy highly and for users who value it less. One of our main results in the case of a single site is that, under normally distributed utilities, if a privacy-sensitive user is worth at least √ 2−1 timesasmuch to advertisers as a privacy-insensitive user, the site operator should strive to make the cost of a privacy option as low as possible. In the case of multiple sites, we show how a Prisoner’s-Dilemma situation can arise: In the equilibrium in which both sites are obliged to offer a privacy option at minimal cost, both sites obtain lower revenue than they would if they colluded and neither offered a privacy option. 1
