Context-Aware Computing via Mobile Social Cloud

The realm of context-aware computing through the mobile social cloud, as Prof. Rick Han from the University of Colorado at Boulder delves into the intricate interplay of mobile social networks, the SocialFusion project, distributing SocialFusion in the cloud, and the importance of privacy in the context-aware mobile cloud. Uncover research challenges such as tracking important attributes, group inference, and recommendations, individual and group privacy concerns, and the quest for easy and reusable context-aware app development. Dive into a landscape where mobile, social, and cloud technologies converge to shape the future of computing

• Context-Aware Computing
• Mobile Social Cloud
• Social Networks
• Privacy Concerns
• Research Challenges

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Presentation Transcript

1. Towards Context-Aware Computing via the Mobile Social Cloud Prof. Rick Han University of Colorado at Boulder

2. Context-Aware Mobile Social Networks Social Networks Who? Where? -My Preferences -My Friends Context-Aware System Context-Aware Mobile Applications

3. The SocialFusion Project What? Sensor activity, Mood, etc. Social Networks Who? Where? -My Preferences -My Friends SocialFusion [HotMobile10] Individually Context-Aware App s Group Context-Aware App s Context-Aware Group Video SocialAwareFlicks [MS09] Context-Aware Group Audio - Music Jukebox New apps WhozThat? Mobile Social Network [IEEENetwork08] Loopt, Foursquare, etc.

4. Distributing SocialFusion in the Cloud Who? What? Who? What? Who? What? Where? Where? Where? Mobile Cloud Services (Context- Aware), e.g. Azure, EC2, etc. DB Distributed Data Mining Inference Layer DM1 DM2 DMN DML2 DML2 Recommendation Or Actuation Layer REC1 REC2 REC2 [CUTechReport09] RECX Context-Aware App s Context-Aware Group Video And Audio Apps Minority Report App Mobile Social Networks

5. Privacy in the Context-Aware Mobile Cloud Who? What? Where? PRIVACY PRIVACY PRIVACY Aaron Beach: PP-Anonymity vs. K-Anonymity DB Group Privacy Issues, anonymize before releasing to 3rd parties downstream PRIVACY ANONYMIZATION Data Mining/Inference Recommendation Context-Aware Mobile App s

6. CawbWeb - The Context-Aware Mobile Web/Cloud How do we make developing a context-aware app easy? (and the results reusable) Specify what you want to do in CawbWeb, e.g. in a series of intentions Compile these intentions into a working app Compiler hides details of how to compose Web services, e.g. WSDL, etc. [CawbWeb CU Tech Report 2010]

7. Summary Research challenges encountered (and not solved): What are the important attributes to track? Group inference and recommendation Privacy of individuals and groups How to make building context-aware app s easy and reusable? rhan@cs.colorado.edu