Mingxuan Yuan

Protecting Sensitive Labels in Social Network Data Anonymization

Privacy is one of the major concerns when publishing or sharing social network data for social science research and business analysis. Recently, researchers have developed privacy models similar to k-anonymity to prevent node reidentification through structure information. However, even when these privacy models are enforced, an attacker may still be able to infer ones private information if a group of nodes largely share the same sensitive labels (i.e., attributes). In other words, the label-node relationship is not well protected by pure structure anonymization methods. Furthermore, existing approaches, which rely on edge editing or node clustering, may significantly alter key graph properties. In this paper, we define a k-degree-l-diversity anonymity model that considers the protection of structural information as well as sensitive labels of individuals. We further propose a novel anonymization methodology based on adding noise nodes. We develop a new algorithm by adding noise nodes into the original graph with the consideration of introducing the least distortion to graph properties. Most importantly, we provide a rigorous analysis of the theoretical bounds on the number of noise nodes added and their impacts on an important graph property. We conduct extensive experiments to evaluate the effectiveness of the proposed technique.

Telco Churn Prediction with Big Data

We show that telco big data can make churn prediction much more easier from the

Differential privacy in telco big data platform

3

OceanST: a distributed analytic system for large-scale spatiotemporal mobile broadband data

Vs perspectives: Volume, Variety, Velocity. Experimental results confirm that the prediction performance has been significantly improved by using a large volume of training data, a large variety of features from both business support systems (BSS) and operations support systems (OSS), and a high velocity of processing new coming data. We have deployed this churn prediction system in one of the biggest mobile operators in China. From millions of active customers, this system can provide a list of prepaid customers who are most likely to churn in the next month, having

City-Scale Localization with Telco Big Data

0.96

Telco User Activity Level Prediction with Massive Mobile Broadband Data

precision for the top

Sampling Big Trajectory Data

50000

Materialization and Decomposition of Dataspaces for Efficient Search

predicted churners in the list. Automatic matching retention campaigns with the targeted potential churners significantly boost their recharge rates, leading to a big business value.

Enabling Scalable Geographic Service Sharing with Weighted Imprecise Voronoi Cells

Differential privacy (DP) has been widely explored in academia recently but less so in industry possibly due to its strong privacy guarantee. This paper makes the first attempt to implement three basic DP architectures in the deployed telecommunication (telco) big data platform for data mining applications. We find that all DP architectures have less than 5% loss of prediction accuracy when the weak privacy guarantee is adopted (e.g., privacy budget parameter e ≥ 3). However, when the strong privacy guarantee is assumed (e.g., privacy budget parameter e ≤ 0:1), all DP architectures lead to 15% ~ 30% accuracy loss, which implies that real-word industrial data mining systems cannot work well under such a strong privacy guarantee recommended by previous research works. Among the three basic DP architectures, the Hybridized DM (Data Mining) and DB (Database) architecture performs the best because of its complicated privacy protection design for the specific data mining algorithm. Through extensive experiments on big data, we also observe that the accuracy loss increases by increasing the variety of features, but decreases by increasing the volume of training data. Therefore, to make DP practically usable in large-scale industrial systems, our observations suggest that we may explore three possible research directions in future: (1) Relaxing the privacy guarantee (e.g., increasing privacy budget e) and studying its effectiveness on specific industrial applications; (2) Designing specific privacy scheme for specific data mining algorithms; and (3) Using large volume of data but with low variety for training the classification models.

Dissecting Regional Weather-Traffic Sensitivity Throughout a City

With the increasing prevalence of versatile mobile devices and the fast deployment of broadband mobile networks, a huge volume of Mobile Broadband (MBB) data has been generated over time. The MBB data naturally contain rich information of a large number of mobile users, covering a considerable fraction of whole population nowadays, including the mobile applications they are using at different locations and time; the MBB data may present the unprecedentedly large knowledge base of human behavior which has highly recognized commercial and social value. However, the storage, management and analysis of the huge and fast growing volume of MBB data post new and significant challenges to the industrial practitioners and research community. In this demonstration, we present a new, MBB data tailored, distributed analytic system named OceanST which has addressed a series of problems and weaknesses of the existing systems, originally designed for more general purpose and capable to handle MBB data to some extent. OceanST is featured by (i) efficiently loading of ever-growing MBB data, (ii) a bunch of spatiotemporal aggregate queries and basic analysis APIs frequently found in various MBB data application scenarios, and (iii) sampling-based approximate solution with provable accuracy bound to cope with huge volume of MBB data. The demonstration will show the advantage of OceanST in a cluster of 5 machines using 3TB data.