Title: Personal Data: A New Asset Class
I will describe a multi-year project with the World Economic Forum, US White House, and central EU authorities to understand the impact of `reality mining' personal data such as location or purchase data, and the emerging broad agreement about how to protect citizens and yet support commerce by use of trust networks. Surprising examples of the power of reality mining will be used to illustrate problems that are not generally appreciated.
Title: Trust & Privacy enhancing technologies
The course is about privacy-aware access control mechanisms with a particular focus on information managed by (Geo) Social Network services. Indeed, the advent of social computing and, more recently, the social web vision require a new paradigm of access control, in which interpersonal, user-to-resources and trust relationships are explicitly tracked by the protection system for the purpose of access control.
The course will briefly recap the traditional mechanisms for access control by discussing why they do not fit in the On-line Social Network (OSN) scenario. It will then discuss, the requirement of privacy-preserving access control mechanisms for OSNs both in terms of privacy preference specification and architecture for their enforcement (centralized vs decentralized) and review some research proposals in the field. The concept of relationship-based access control will be introduced and its advantages and drawbacks will be discussed. Moving one step further, the course will discuss the fundamental role of trust relationships in the controlled release of personal information and the underlying trust models. Privacy-preserving trust computation will also be addressed. Finally, the course will introduce the novel concept of risk-aware privacy settings and its interplay with trust.
Title: Models of social complexity
Part 1: Social network analysis: a crash course, Dino Pedreschi
Over the past decade there has been a growing public fascination with the complex "connectedness" of modern society. This connectedness is found in many contexts: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity. These are phenomena that involve networks and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which each of our decisions can have subtle consequences for the outcomes of everyone else.
This crash mini-course is an introduction to the analysis of complex networks, with a special focus on the social network and its structure and function. Drawing on ideas from computing and information science, applied mathematics, economics and sociology, this lecture sketchily describes the emerging field of study that is growing at the interface of all these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected.
- Big graph data and social, information, biological and technological networks
- How real networks differ from random: node degree and long tails, social distance and small worlds, clustering and triadic closure. Comparing real networks and random graphs.
- Strong and weak ties, community structure and long-range bridges. The strength of weak ties for the diffusion of information. The strength of the strong ties for the diffusion of innovation.
- The correlation between the social network and human mobility.
- Centrality measures: what are the important nodes in a network?
- Network dynamics: power laws and "rich get richer" phenomenon, the "small-world" phenomenon.
Part 2: Crowds and emergent behaviors, Dirk Helbing
- Emergence of cooperation and social cohesion
- Wisdom of the crowds and social influence
- Pedestrian behavior, crowd mobility and disasters
- Emergence of social norms, conflicts, and revolutions
- The FuturICT vision: revealing the hidden laws and processes underlying societies www.futurict.eu
Title: Privacy-preserving mobilytics
The lecture shall focus on the issue of privacy and anonymity in location-aware and movement-aware data.
Specifically, the lecture will provide an overview of the traditional models proposed in literature to address the privacy issues in relational database and then will focus on the application of these models in mobility data. The lecture is organized in two parts:
- privacy and anonymity in mobility data analysis: models of privacy and anonymity threats for mobility data publishing and data mining where the dual goal is to guarantee the privacy while preserving a reasonable data quality;
- on-line protection of location privacy: after a brief overview of major paradigms for the protection of location privacy in real time, including identity and location privacy, we introduce recent research on semantic-aware location privacy. We finally propose a case discussion related to the development of geo-location standards for on-line applications.
- Privacy Models in Relational Data
- Privacy and anonymity in mobility data analysis
- Location privacy
- On-line protection of location privacy
Part 1: Mobility Atlas of a city
Several datasources: different point of views
- Road side sensors
- GSM network logs
- GPS tracked vehicles
General laws of mobility
- Individual mobility
How much people travel? (short, medium, long trips, radius of gyration) - Differences between small and large cities
- Access pattern to a city
How they change during day, week, month?
Which user profiles at the access gates to the city?
- Car Pooling service 15
- Tourism Observatory
- Validation of datasources 5
Behind the scene: M-Atlas and mobility data management
- Mobility data mining
algorithm and methods
- Case study: How to implement a service with the Mobility Atlas
Part 2 Visual Analytics of Spatio-Temporal Data (including Visual Analytics of Movement)
1. Overview of visual analytics
2. Types of spatio-temporal data, structure of task space
3. Spatial time series
3.1. Animated maps
3.2. Diagram maps
3.3. Small multiples
3.4. Time graphs
3.5. Clustering of places and time intervals
4.1. Visualization methods
4.2. Analysis methods (ST aggregation, density surface, ST clustering,
5.1. Space-time cube
5.2. Clustering trajectories
5.3. Dynamic transformation of time
5.4. Semantic annotation of trajectories
5.5. Event extraction and analysis
5.6. Potentially useful transformations
6. Concluding remarks
Effective Big Data management and analysis poses several difficult challenges for modern database infrastructures. One key such challenge arises from the naturally streaming nature of big data, which mandates efficient algorithms for querying and analyzing massive, continuous data streams (that is, data that is seen only once and in a fixed order) with limited memory and CPU-time resources. Such streams arise naturally in emerging large-scale event monitoring applications; for instance, network-operations monitoring in large ISPs, where usage information from numerous sites needs to be continuously collected and analyzed for interesting trends. In addition to memory- and time-efficiency concerns, the inherently distributed nature of such applications also raises important communication-efficiency issues, making it critical to carefully optimize the use of the underlying network infrastructure.
This seminar will give an overview of some key algorithmic tools for effective query processing over streaming big data. The focus will be on small-space sketching structures for approximating continuous data streams in both centralized and distributed settings.
- Introduction and Motivation: Big Data and Data Streams (20mins)
Part I: Centralized Data Streaming (75mins)
* Key Data Streaming Models
* Basic Stream Synopses: Sampling; AMS, FM, and CountMin sketches
Part II: Distributed Data Streaming (75mins)
* Distributed Streams and Continuous Distributed Monitoring (CDM)
* CDM using AMS Sketches
* Geometric Distributed Stream Monitoring
- Conclusions and Future Directions (10mins)