Protecting Privacy by Secure Computation

Privacy Protection Measures and Technologies in Business Organizations ◽

10.4018/978-1-61350-501-4.ch011 ◽

2012 ◽

pp. 268-284

Author(s):

Florian Kerschbaum ◽

Daniel Funke

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Relationship Management ◽

Secure Computation ◽

Customer Relationship ◽

System A ◽

Input And Output ◽

Social Network Structure ◽

New Information

We consider collaborative social network analysis without revealing private inputs of the participants. This problem arises in criminal investigations of federal police organization where single organizations may not reveal their data without probable cause, but the aggregation of all data entails new information, such as the entire social network structure. We present algorithms for securely computing either the entire, anonymized graph or only specific metrics for individuals. We use secure computation protocols to disclose nothing, but the output of the analysis, i.e. anything that cannot be derived from one’s input and output – including other parties’ input – remains private. We have implemented a prototype for SAP’s investigative case management system – a derivate of its customer relationship management.

Download Full-text

ANALISA INTERAKSI USER DI MEDIA SOSIAL MENGENAI INDUSTRI FINTECH MENGGUNAKAN SOCIAL NETWORK ANALYSIS (STUDI KASUS: GOPAY, OVO DAN LINKAJA)

Jurnal Mitra Manajemen ◽

10.52160/ejmm.v4i3.352 ◽

2020 ◽

Vol 4 (3) ◽

pp. 341-351

Author(s):

Alisya Putri Rabbani ◽

Andry Alamsyah ◽

Sri Widiyanesti

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Customer Relationship Management ◽

Word Of Mouth ◽

Relationship Management ◽

Customer Relationship ◽

Customer Engagement ◽

Electronic Word Of Mouth ◽

Financial Technology

Financial technology (Fintech) mengalami pertumbuhan yang cukup pesat sejak awal kehadirannya di Indonesia. Fintech merupakan industri jasa finansial yang memanfaatkan teknologi sehingga memungkinkan penggunanya melakukan berbagai transaksi keuangan secara digital. Saat ini banyak fintech baru yang bermunculan di Indonesia, sehingga dibutuhkan strategi yg tepat untuk bisa bersaing dgn kompetitor. Analisis interaksi pengguna media sosial, biasa disebut dengan Electronic Word of Mouth (EWOM) dapat memberikan informasi yang dapat mendukung berbagai keputusan bisnis, salah satunya adalah terkait customer engagement. Tujuan dari penelitian ini adalah mengidentifikasi customer engagement yang terbentuk dari hasil implementasi Social Customer Relationship Management (SCRM) yang dilakukan oleh perusahaan. Data yang digunakan dalam penelitian ini adalah data sekunder yang merupakan data tweets berisi interaksi pengguna twitter mengenai 3 fintech di Indonesia yaitu GoPay, OVO, dan LinkAja. Analisis data dilakukan peneliti mengunakan metode social network analysis dengan menghitung properti jaringan dari ketiga objek penelitian. Hasil menunjukkan bahwa LinkAja mebentuk customer engagement lebih optimal lewat implementasi SCRM yang dilakukan perusahaan.

Download Full-text

Analisa Interaksi Pengguna Media Sosial Perusahaan Sekuritas di Indonesia Saat Covid-19 menggunakan Social Network Analysis (Studi Kasus: Indopremier dan Bursa Efek Indonesia)

Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) ◽

10.35870/jtik.v5i1.166 ◽

2020 ◽

Vol 4 (2) ◽

pp. 72

Author(s):

Della Diniyati ◽

Agung Triayudi ◽

Ira Diana Sholihati

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Customer Relationship Management ◽

Interaction Analysis ◽

Relationship Management ◽

Customer Relationship ◽

User Generated Content ◽

Customer Engagement ◽

Twitter User

The development of information technology, especially social media users, is increasing. Many securities companies make use of technology so that users can make various transactions and search for information digitally. This can be used in online marketing strategies and information dissemination, one of which is Twitter. Users can disclose known information and this information is User Generated Content (UGC), which is the track record left behind. Twitter user interaction analysis can provide information that supports various business decisions, such as customer engagement. This research takes advantage of the Covid-19 phenomenon in which the stock market has experienced a global downturn. The purpose of this study is to determine the level of awareness and identify customer engagement from the results of the implementation of Social Customer Relationship Management (SCRM) by the company. This study uses the Social Network Analysis (SNA) method, with secondary data in the form of tweets of Twitter user interactions regarding 2 securities companies, IndoPremier, and the Indonesia Stock Exchange. The result is that IndoPremier is a securities company that is superior in informing optimal customer engagement through the implementation of SCRM.Keywords:Social Network Analysis, User Generated Content, Social Customer Relationship Management, COVID-19.

Download Full-text

Social Network Analysis

Encyclopedia of Knowledge Management, Second Edition ◽

10.4018/978-1-59904-931-1.ch139 ◽

2011 ◽

pp. 1460-1469

Author(s):

David J. Dekker ◽

Paul H.J. Hendriks

Keyword(s):

Knowledge Management ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Development Project ◽

Knowledge Networks ◽

Knowledge Processes ◽

Social Network Structure ◽

Product Development Project ◽

Practice Knowledge

In knowledge management (KM), one perspective is that knowledge resides in individuals who interact in groups. Concepts as communities-of-practice, knowledge networks, and “encultured knowledge” as the outcome of shared sense-making (Blackler, 1995) are built upon this perspective. Social network analysis focuses on the patterns of people’s interactions. This adds to KM theory a dimension that considers the effects of social structure on for example, knowledge creation, retention and dissemination. This article provides a short overview of consequences of social network structure on knowledge processes and explores how the insights generated by social network analysis are valuable to KM as diagnostic elements for drafting KM interventions. Relevance is apparent for management areas such as R&D alliances, product development, project management, and so forth.

Download Full-text

The Research of Social Network Analysis on College Students' Interactive Relations

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/ijcini.20210401.oa5 ◽

2021 ◽

Vol 15 (2) ◽

pp. 49-59

Author(s):

Chang Chen ◽

Min Chen

Keyword(s):

College Students ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Student Affairs ◽

Interpersonal Relations ◽

Interaction Network ◽

Information Support ◽

Limited Information ◽

New Information

Nowadays the number of college students' suicides are increasing for the insufficient social support or poor interpersonal relations. Furthermore, not much attention has been concerned to students' interpersonal relations when handling student affairs and only very limited information about students' interaction network is available. This paper studies the peer network of college students by using the tool of social network analysis. And it aims to serve as instrumental support for students to foster and develop harmonious interpersonal relations. It offers new information for school counsellor to better handle student affairs and provides information support for the carrying out of moral and ideological guidance for students.

Download Full-text

Cluster Based Architecture and Algorithm to Improve the Design of Social Networks

International Journal of Virtual Communities and Social Networking ◽

10.4018/ijvcsn.2017070103 ◽

2017 ◽

Vol 9 (3) ◽

pp. 29-43

Author(s):

Saurab Dutta ◽

Payel Roy

Keyword(s):

Social Networks ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Low Cost ◽

Customer Relationship ◽

Management Tool ◽

Goods And Services ◽

Increasing Demand ◽

Business Purpose

In a social network people are connected by relationships, business purpose or transaction activity. The increasing demand of social network analysis and how to improve the architecture is of utmost importance for the organizations who are regularly trying to improve the service through social network analysis. Social network analysis views social relationship in terms of network theory. Social networks connect people at very low cost and this network acts as a customer relationship management tool for increasing sales of organization in terms of goods and services. Different models are proposed and utilized in different platforms. In this model, the authors have proposed a cluster-based structure to improve performance of social networks.

Download Full-text

Social Network Analysis

Encyclopedia of Knowledge Management ◽

10.4018/978-1-59140-573-3.ch107 ◽

2011 ◽

pp. 818-825

Author(s):

David J. Dekker ◽

Paul H.J. Hendriks

Keyword(s):

Knowledge Management ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Development Project ◽

Knowledge Networks ◽

Knowledge Processes ◽

Social Network Structure ◽

Product Development Project ◽

Practice Knowledge

In knowledge management (KM), one perspective is that knowledge resides in individuals who interact in groups. Concepts as communities-of-practice, knowledge networks, and “encultured knowledge” as the outcome of shared sense-making (Blackler, 1995) are built upon this perspective. Social network analysis focuses on the patterns of people’s interactions. This adds to KM theory a dimension that considers the effects of social structure on for example, knowledge creation, retention and dissemination. This article provides a short overview of consequences of social network structure on knowledge processes and explores how the insights generated by social network analysis are valuable to KM as diagnostic elements for drafting KM interventions. Relevance is apparent for management areas such as R&D alliances, product development, project management, and so forth.

Download Full-text

Social Network Analysis (SNA) for Churn Mitigation/Detection

Developing Churn Models Using Data Mining Techniques and Social Network Analysis ◽

10.4018/978-1-4666-6288-9.ch004 ◽

2014 ◽

pp. 63-141

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Customer Relationship ◽

Full Potential ◽

Complex Environments ◽

Churn Analysis ◽

Advanced Analytics ◽

Churn Management

Social network analysis is intentionally covered in a separate chapter for two reasons. First, the importance of this method has rapidly increased in past few years, and second, there are very few useable studies that cover social network analysis concepts in churn management. By understanding the methods explained in Chapter 3 and combining them with knowledge of SNA concepts, the analysts (readers) can unlock the full potential of advanced analytics in one of the most important fields of research today, customer relationship and especially churn analysis. With the ability to understand how those metrics can be used, integration of those methods into more complex environments is explained regarding the key topic, churn management.

Download Full-text