81,522 research outputs found
A practical and secure multi-keyword search method over encrypted cloud data
Cloud computing technologies become more and more popular every year, as many organizations tend to outsource their data utilizing robust and fast services of clouds while lowering the cost of hardware ownership. Although its benefits are welcomed, privacy is still a remaining concern that needs to be addressed. We propose an efficient privacy-preserving search method over encrypted cloud data that utilizes minhash functions. Most of the work in literature can only support a single feature search in queries which reduces the effectiveness. One of the main advantages of our proposed method is the capability of multi-keyword search in a single query. The proposed method is proved to satisfy adaptive semantic security definition. We also combine an effective ranking capability that is based on term frequency-inverse document frequency (tf-idf) values of keyword document pairs. Our analysis demonstrates that the proposed scheme is proved to be privacy-preserving, efficient and effective
Decentralized Exploration in Multi-Armed Bandits
We consider the decentralized exploration problem: a set of players
collaborate to identify the best arm by asynchronously interacting with the
same stochastic environment. The objective is to insure privacy in the best arm
identification problem between asynchronous, collaborative, and thrifty
players. In the context of a digital service, we advocate that this
decentralized approach allows a good balance between the interests of users and
those of service providers: the providers optimize their services, while
protecting the privacy of the users and saving resources. We define the privacy
level as the amount of information an adversary could infer by intercepting the
messages concerning a single user. We provide a generic algorithm Decentralized
Elimination, which uses any best arm identification algorithm as a subroutine.
We prove that this algorithm insures privacy, with a low communication cost,
and that in comparison to the lower bound of the best arm identification
problem, its sample complexity suffers from a penalty depending on the inverse
of the probability of the most frequent players. Then, thanks to the genericity
of the approach, we extend the proposed algorithm to the non-stationary
bandits. Finally, experiments illustrate and complete the analysis
Privacy Management and Optimal Pricing in People-Centric Sensing
With the emerging sensing technologies such as mobile crowdsensing and
Internet of Things (IoT), people-centric data can be efficiently collected and
used for analytics and optimization purposes. This data is typically required
to develop and render people-centric services. In this paper, we address the
privacy implication, optimal pricing, and bundling of people-centric services.
We first define the inverse correlation between the service quality and privacy
level from data analytics perspectives. We then present the profit maximization
models of selling standalone, complementary, and substitute services.
Specifically, the closed-form solutions of the optimal privacy level and
subscription fee are derived to maximize the gross profit of service providers.
For interrelated people-centric services, we show that cooperation by service
bundling of complementary services is profitable compared to the separate sales
but detrimental for substitutes. We also show that the market value of a
service bundle is correlated with the degree of contingency between the
interrelated services. Finally, we incorporate the profit sharing models from
game theory for dividing the bundling profit among the cooperative service
providers.Comment: 16 page
Optimal Auction Design and Irrelevance of Private Information
We consider the problem of mechanism design by a principal who has private information. We point out a simple condition under which the privacy of the principal's information is irrelevant in the sense that the mechanism implemented by the principal coincides with the mechanism that would be optimal if the principal's information were publicly known. This condition is then used to show that the privacy of the principal's information is irrelevant in many environments with private values and quasi-linear preferences, including the Myerson's classical auction environments in which the seller is privately informed about her cost of selling. Our approach unifies results by Maskin and Tirole, Tan, Yilankaya, Skreta, and Balestrieri. We also provide an example of a classical principal-agent environment with private values and quasi-linear preferences where a privately informed principal can do better than when her information is public.independent private values, optimal auction, resale, inverse virtual valuation function
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