Data Mining - Concepts and Techniques.

Following the 2016 U.S. presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: (i) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their “most important” source; (ii) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; (iii) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and (iv) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks.

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Social Media and Fake News in the 2016 Election

https://andreashellander.se/wp-content/uploads/2016/01/1-s2.0-S0306437914001288-main.pdf

The rise of big data on cloud computing

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

There is no doubt that big data are now rapidly expanding in all science and engineering domains. While the potential of these massive data is undoubtedly significant, fully making sense of them requires new ways of thinking and novel learning techniques to address the various challenges. In this paper, we present a literature survey of the latest advances in researches on machine learning for big data processing. First, we review the machine learning techniques and highlight some promising learning methods in recent studies, such as representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning. Next, we focus on the analysis and discussions about the challenges and possible solutions of machine learning for big data. Following that, we investigate the close connections of machine learning with signal processing techniques for big data processing. Finally, we outline several open issues and research trends.

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A survey of machine learning for big data processing

While "big data" has become a highlighted buzzword since last year, "big data mining", i.e., mining from big data, has almost immediately followed up as an emerging, interrelated research area. This paper provides an overview of big data mining and discusses the related challenges and the new opportunities. The discussion includes a review of state-of-the-art frameworks and platforms for processing and managing big data as well as the efforts expected on big data mining. We address broad issues related to big data and/or big data mining, and point out opportunities and research topics as they shall duly flesh out. We hope our effort will help reshape the subject area of today's data mining technology toward solving tomorrow's bigger challenges emerging in accordance with big data.

From Big Data to Big Data Mining: Challenges, Issues, and Opportunities

A privacy-preserved full-text retrieval algorithm over encrypted data for cloud storage applications

Motivated by the application of viral marketing, the topic-aware influence maximization (TIM) problem has been proposed to identify the most influential users under given topics. In particular, it aims to find k seeds (users) in social network G, such that the seeds can maximize the influence on users under the specific query topics and diffusion model such as independent cascade (IC) or linear threshold (LT).
This problem has been proved to be NP-hard, and most of the proposed techniques suffer from the efficiency issue due to the lack of generalization. Even worse, the design of these algorithms requires significant specialized knowledge which is hard to be understood and implemented. To overcome these issues, this paper aims to learn a generalized heuristic framework to solve TIM problems by meta-learning. To this end, we first propose two topic-aware social influence propagation models based on IC and LT model, respectively, which is conducive to better advertising injections. We then encode the feature of each node by a vector and introduce a model, called deep influence evaluation model , to evaluate the user influence under different circumstances. Based on this model, we can construct the solution according to the influence evaluations efficiently, rather than spending a high cost to compute the exact influence by considering the complex graph structure. We conducted experiments on generated graph instances and real-world social networks. The results show the superiority in performance and comparable quality of our framework.

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Deep Reinforcement Learning-Based Approach to Tackle Topic-Aware Influence Maximization

Geographical data is of great value for data producers. How to protect copyright of geographical data effectively using digital watermarking is a hot research issue. In this paper, spatial topological relation existing between polygons is chosen as cover data, and watermark is embedded by slightly modifying the metric measure of spatial topological relation, and some geographical objects are scaled to derive the watermarked data. Global Main Scaling Distance is introduced to measure the distortions caused by watermark embedding. Experimental results show that the proposed algorithm has a good robustness against geometrical attacks, simplification, interpolation and noise addition attacks, and preserves shape of the watermarked geographical objects. Furthermore, a good tradeoff between robustness and fidelity is acquired in the proposed algorithm.

Watermarking geographical data on spatial topological relations

This paper discusses the feasibility of embedding a bit map image (BMP file) into the relational databases for protecting data's copyrights and the term of WDI (Watermarking Databases using Image) is proposed. An error correction approach of BCH (Bose-Chaudhuri-Hocquenhem) coding is used for enhancing the robustness of the algorithms. And a Trusted Third Party (TTP), which can trigger watermarking mobile agents to insert and detect watermark, is introduced to resist the additive attack and invertibility attack. Further more, we also analyze the resilience of the algorithm theoretically based on the principles of statistics in detail. Experiments showed that the approach proposed in this paper is robust to many kinds of attacks so that the copyrights can be effectively protected.

Zhiyong Peng

Papers

From Big Data to Big Data Mining: Challenges, Issues, and Opportunities

A privacy-preserved full-text retrieval algorithm over encrypted data for cloud storage applications

Deep Reinforcement Learning-Based Approach to Tackle Topic-Aware Influence Maximization

Watermarking geographical data on spatial topological relations

An additive-attack-proof watermarking mechanism for databases' copyrights protection using image