45 research outputs found
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. 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 micro-payments, and characterize valid solutions.Comment: 25 pages, 2 figures. Best Paper Award, to appear in the 16th
International Conference on Database Theory (ICDT), 201
User-centric Privacy Engineering for the Internet of Things
User privacy concerns are widely regarded as a key obstacle to the success of
modern smart cyber-physical systems. In this paper, we analyse, through an
example, some of the requirements that future data collection architectures of
these systems should implement to provide effective privacy protection for
users. Then, we give an example of how these requirements can be implemented in
a smart home scenario. Our example architecture allows the user to balance the
privacy risks with the potential benefits and take a practical decision
determining the extent of the sharing. Based on this example architecture, we
identify a number of challenges that must be addressed by future data
processing systems in order to achieve effective privacy management for smart
cyber-physical systems.Comment: 12 Page
How to Balance Privacy and Money through Pricing Mechanism in Personal Data Market
A personal data market is a platform including three participants: data
owners (individuals), data buyers and market maker. Data owners who provide
personal data are compensated according to their privacy loss. Data buyers can
submit a query and pay for the result according to their desired accuracy.
Market maker coordinates between data owner and buyer. This framework has been
previously studied based on differential privacy. However, the previous study
assumes data owners can accept any level of privacy loss and data buyers can
conduct the transaction without regard to the financial budget. In this paper,
we propose a practical personal data trading framework that is able to strike a
balance between money and privacy. In order to gain insights on user
preferences, we first conducted an online survey on human attitude to- ward
privacy and interest in personal data trading. Second, we identify the 5 key
principles of personal data market, which is important for designing a
reasonable trading frame- work and pricing mechanism. Third, we propose a
reason- able trading framework for personal data which provides an overview of
how the data is traded. Fourth, we propose a balanced pricing mechanism which
computes the query price for data buyers and compensation for data owners
(whose data are utilized) as a function of their privacy loss. The main goal is
to ensure a fair trading for both parties. Finally, we will conduct an
experiment to evaluate the output of our proposed pricing mechanism in
comparison with other previously proposed mechanism
The Design of Arbitrage-Free Data Pricing Schemes
Motivated by a growing market that involves buying and selling data over the
web, we study pricing schemes that assign value to queries issued over a
database. Previous work studied pricing mechanisms that compute the price of a
query by extending a data seller's explicit prices on certain queries, or
investigated the properties that a pricing function should exhibit without
detailing a generic construction. In this work, we present a formal framework
for pricing queries over data that allows the construction of general families
of pricing functions, with the main goal of avoiding arbitrage. We consider two
types of pricing schemes: instance-independent schemes, where the price depends
only on the structure of the query, and answer-dependent schemes, where the
price also depends on the query output. Our main result is a complete
characterization of the structure of pricing functions in both settings, by
relating it to properties of a function over a lattice. We use our
characterization, together with information-theoretic methods, to construct a
variety of arbitrage-free pricing functions. Finally, we discuss various
tradeoffs in the design space and present techniques for efficient computation
of the proposed pricing functions.Comment: full pape