arXiv subjects

Masatoshi Yoshikawa

Publications and source records attributed to Masatoshi Yoshikawa.

45 records · Page 3Linked to original sources

arXiv2019

When and where do you want to hide? Recommendation of location privacy preferences with local differential privacy

In recent years, it has become easy to obtain location information quite precisely. However, the acquisition of such information has risks such as individual identification and leakage of sensitive information, so it is necessary to protect the privacy of location information. For this purpose, people should know their location privacy preferences, that is, whether or not he/she can release location information at each place and time. However, it is not easy for each user to make such decisions and it is troublesome to set the privacy preference at each time. Therefore, we propose a method to recommend location privacy preferences for decision making. Comparing to existing method, our method can improve the accuracy of recommendation by using matrix factorization and preserve privacy strictly by local differential privacy, whereas the existing method does not achieve formal privacy guarantee. In addition, we found the best granularity of a location privacy preference, that is, how to express the information in location privacy protection. To evaluate and verify the utility of our method, we have integrated two existing datasets to create a rich information in term of user number. From the results of the evaluation using this dataset, we confirmed that our method can predict location privacy preferences accurately and that it provides a suitable method to define the location privacy preference.

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arXiv2019

Blockchain-based Bidirectional Updates on Fine-grained Medical Data

Electronic medical data sharing between stakeholders, such as patients, doctors, and researchers, can promote more effective medical treatment collaboratively. These sensitive and private data should only be accessed by authorized users. Given a total medical data, users may care about parts of them and other unrelated information might interfere with the user interested data search and increase the risk of exposure. Besides accessing these data, users may want to update them and propagate to other sharing peers so that all peers keep identical data after each update. To satisfy these requirements, in this paper we propose a medical data sharing architecture that addresses the permission control using smart contracts on the blockchain and splits data into fined grained pieces shared with different peers then synchronize full data and these pieces with bidirectional transformations. Medical data reside on each userś local database and permission related data are stored on smart contracts. Only all peers have gained the newest shared data after updates can they start to do next operations on it, which are enforced by smart contracts. Blockchain based immutable shared ledge enables users to trace data updates history. This paper can provide a new perspective to view full medical data as different slices to be shared with various peers but consistency after updates between them are still promised, which can protect the privacy and improve data search efficiency.

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arXiv2018

Beyond Narrative Description: Generating Poetry from Images by Multi-Adversarial Training

Automatic generation of natural language from images has attracted extensive attention. In this paper, we take one step further to investigate generation of poetic language (with multiple lines) to an image for automatic poetry creation. This task involves multiple challenges, including discovering poetic clues from the image (e.g., hope from green), and generating poems to satisfy both relevance to the image and poeticness in language level. To solve the above challenges, we formulate the task of poem generation into two correlated sub-tasks by multi-adversarial training via policy gradient, through which the cross-modal relevance and poetic language style can be ensured. To extract poetic clues from images, we propose to learn a deep coupled visual-poetic embedding, in which the poetic representation from objects, sentiments and scenes in an image can be jointly learned. Two discriminative networks are further introduced to guide the poem generation, including a multi-modal discriminator and a poem-style discriminator. To facilitate the research, we have released two poem datasets by human annotators with two distinct properties: 1) the first human annotated image-to-poem pair dataset (with 8,292 pairs in total), and 2) to-date the largest public English poem corpus dataset (with 92,265 different poems in total). Extensive experiments are conducted with 8K images, among which 1.5K image are randomly picked for evaluation. Both objective and subjective evaluations show the superior performances against the state-of-the-art methods for poem generation from images. Turing test carried out with over 500 human subjects, among which 30 evaluators are poetry experts, demonstrates the effectiveness of our approach.

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arXiv2018

Making View Update Strategies Programmable - Toward Controlling and Sharing Distributed Data -

Views are known mechanisms for controlling access of data and for sharing data of different schemas. Despite long and intensive research on views in both the database community and the programming language community, we are facing difficulties to use views in practice. The main reason is that we lack ways to directly describe view update strategies to deal with the inherent ambiguity of view updating. This paper aims to provide a new language-based approach to controlling and sharing distributed data based on views, and establish a software foundation for systematic construction of such data management systems. Our key observation is that a view should be defined through a view update strategy rather than a view definition. We show that Datalog can be used for specifying view update strategies whose unique view definition can be automatically derived, present a novel P2P-based programmable architecture for distributed data management where updatable views are fully utilized for controlling and sharing distributed data, and demonstrate its usefulness through the development of a privacy-preserving ride-sharing alliance system.

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arXiv2018

A View-based Programmable Architecture for Controlling and Integrating Decentralized Data

The view and the view update are known mechanism for controlling access of data and for integrating data of different schemas. Despite intensive and long research on them in both the database community and the programming language community, we are facing difficulties to use them in practice. The main reason is that we are lacking of control over the view update strategy to deal with inherited ambiguity of view update for a given view. This vision paper aims to provide a new language-based approach to controlling and integrating decentralized data based on the view, and establish a software foundation for systematic construction of such data management systems. Our key observation is that a view should be defined through a view update strategy rather than a query. In other words, the view definition should be extracted from the view update strategy, which is in sharp contrast to the traditional approaches where the view update strategy is derived from the view definition. In this paper, we present the first programmable architecture with a declarative language for specifying update strategies over views, whose unique view definition can be automatically derived, and show how it can be effectively used to control data access, integrate data generally allowing coexistence of GAV (global as view) and LAV (local as view), and perform both analysis and updates on the integrated data. We demonstrate its usefulness through development of a privacy-preserving ride-sharing alliance system, discuss its application scope, and highlight future challenges.

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arXiv2018

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.

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arXiv2017

Quantifying Differential Privacy under Temporal Correlations

Differential Privacy (DP) has received increased attention as a rigorous privacy framework. Existing studies employ traditional DP mechanisms (e.g., the Laplace mechanism) as primitives, which assume that the data are independent, or that adversaries do not have knowledge of the data correlations. However, continuously generated data in the real world tend to be temporally correlated, and such correlations can be acquired by adversaries. In this paper, we investigate the potential privacy loss of a traditional DP mechanism under temporal correlations in the context of continuous data release. First, we model the temporal correlations using Markov model and analyze the privacy leakage of a DP mechanism when adversaries have knowledge of such temporal correlations. Our analysis reveals that the privacy leakage of a DP mechanism may accumulate and increase over time. We call it temporal privacy leakage. Second, to measure such privacy leakage, we design an efficient algorithm for calculating it in polynomial time. Although the temporal privacy leakage may increase over time, we also show that its supremum may exist in some cases. Third, to bound the privacy loss, we propose mechanisms that convert any existing DP mechanism into one against temporal privacy leakage. Experiments with synthetic data confirm that our approach is efficient and effective.

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arXiv2016

Your Neighbors Are My Spies: Location and other Privacy Concerns in Dating Apps

Trilateration has recently become one of the well-known threat models to the user's location privacy in location-based applications (aka: location-based services or LBS), especially those containing highly sensitive information such as dating applications. The threat model mainly depends on the distance shown from the targeted victim to the adversary to pinpoint the victim's position. As a countermeasure, most of location-based applications have already implemented the "hide distance" function to protect their user's location privacy. The effectiveness of such approaches however is still questionable. Therefore, in this paper, we first investigate how popular location-based dating applications are currently protecting their user's privacy by testing the two most popular GLBT-focused applications: Jack'd and Grindr.

cs.CY↗

arXiv2016

Your Neighbors Are My Spies: Location and other Privacy Concerns in GLBT-focused Location-based Dating Applications

Trilateration is one of the well-known threat models to the user's location privacy in location-based apps, especially those contain highly sensitive information such as dating apps. The threat model mainly bases on the publicly shown distance from a targeted victim to the adversary to pinpoint the victim's location. As a countermeasure, most of location-based apps have already implemented the 'hide distance' function, or added noise to the publicly shown distance in order to protect their user's location privacy. The effectiveness of such approaches however is still questionable.

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