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

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.Comment: appears at ICDE 201

Quantifying Differential Privacy in Continuous Data Release under Temporal Correlations

Differential Privacy (DP) has received increasing attention as a rigorous privacy framework. Many existing studies employ traditional DP mechanisms (e.g., the Laplace mechanism) as primitives to continuously release private data for protecting privacy at each time point (i.e., event-level privacy), which assume that the data at different time points are independent, or that adversaries do not have knowledge of correlation between data. However, continuously generated data 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. First, we analyze the privacy leakage of a DP mechanism under temporal correlation that can be modeled using Markov Chain. Our analysis reveals that, the event-level privacy loss of a DP mechanism may \textit{increase over time}. We call the unexpected privacy loss \textit{temporal privacy leakage} (TPL). Although TPL may increase over time, we find that its supremum may exist in some cases. Second, we design efficient algorithms for calculating TPL. Third, we propose data releasing mechanisms that convert any existing DP mechanism into one against TPL. Experiments confirm that our approach is efficient and effective.Comment: accepted in TKDE special issue "Best of ICDE 2017". arXiv admin note: substantial text overlap with arXiv:1610.0754

A Generalized Flow-Based Method for Analysis of Implicit Relationships on Wikipedia

We focus on measuring relationships between pairs of objects in Wikipedia whose pages can be regarded as individual objects. Two kinds of relationships between two objects exist: in Wikipedia, an explicit relationship is represented by a single link between the two pages for the objects, and an implicit relationship is represented by a link structure containing the two pages. Some of the previously proposed methods for measuring relationships are cohesion-based methods, which underestimate objects having high degrees, although such objects could be important in constituting relationships in Wikipedia. The other methods are inadequate for measuring implicit relationships because they use only one or two of the following three important factors: distance, connectivity, and cocitation. We propose a new method using a generalized maximum flow which reflects all the three factors and does not underestimate objects having high degree. We confirm through experiments that our method can measure the strength of a relationship more appropriately than these previously proposed methods do. Another remarkable aspect of our method is mining elucidatory objects, that is, objects constituting a relationship. We explain that mining elucidatory objects would open a novel way to deeply understand a relationship

Prerequisite-aware course ordering towards getting relevant job opportunities

Adapting learning experience according to the rapidly-changing job market is essential for students to achieve fruitful learning and successful career development. As building blocks of potential job opportunities, we focus on “technical terminologies” which are frequently required in the job market. Given a technical terminology, we aim at identifying an order of courses which contributes to the acquisition of knowledge about the terminology and also follows the prerequisite relationships among courses. To solve the course ordering problem, we develop a two-step approach, in which course-terminology relatedness is first estimated and then courses are ordered based on the prerequisite relationships and the estimated relatedness. Focusing on the second step, we propose a method based on Markov decision process (MDPOrd) and compare it with three other methods: (1) a method that orders courses based on aggregated relatedness (AggRelOrd), (2) a method that topologically sorts the courses based on personalized PageRank values (PageRankTS), and (3) a method that greedily picks courses based on the average relatedness (GVPickings). In addition to evaluating how the order prioritizes the related courses, we also evaluate from pedagogical perspectives, namely, how the order prioritizes specifically/generally fundamental courses, and how it places courses close to their prerequisites. Experimental results on two course sets show that MDPOrd outperforms the other methods in prioritizing related courses. In addition, MDPOrd is effective in ordering courses close to their prerequisites, but does not work well in highly ranking fundamental courses in the order