In the era of Big Data, every single user of our hyper-connected world leaves behind a myriad of digital breadcrumbs while performing her daily activities. Nowadays, a simple smartphone enables each one of us to browse the Web, listen to music on online musical services, post messages on social networks, perform online shopping, acquire images and record our geo\-graphical locations. This enormous amount of personal data can be exploited to improve the lifestyle of each individual by extracting, analyzing and exploiting user's behavioral patterns like the items frequently purchased, the routinary movements, the favorite sequence of songs listened, etc. Up to now, the highly valuable personal patterns able to predict human behavior can only be extracted by big companies, which employ this information mainly to improve marketing strategies. This organization-centric model does not empower to take full advantage of the possibility of knowledge extraction offered by personal data, mainly because each company has only a limited view on individuals that is restricted to the type of data for which the company provides services. Moreover, users have a very limited capability to control and exploit their personal data. Although some user-centric models like the Personal Information Management System and the Personal Data Store are emerging, currently there is still a significant lack in terms of algorithms and models specifically designed to capture the knowledge from individual data and to ensure privacy protection in a user-centric scenario.
Personal data analytics and individual privacy protection are the key elements to leverage nowadays services to a new type of systems. The availability of personal analytics tools able to extract hidden knowledge from individual data while protecting the privacy right can help the society to move from organization-centric systems to user-centric systems, where the user is the owner of her personal data and is able to manage, understand, exploit, control and share her own data and the knowledge deliverable from them in a completely safe way.
The purpose of this workshop is to encourage principled research that will lead to the advancement of personal data analytics, personal services development, privacy, data protection and privacy risk assessment. The workshop will seek top-quality submissions addressing important issues related to personal analytics, personal data mining and privacy in the context where real individual data (spatio-temporal data, call details records, tweets, mobility data, social networking data, etc.) are used for developing a data-driven service, for realizing a social study aimed at understanding nowadays society, and for publication purposes. Papers can present research results in any of the themes of interest for the workshop as well as application experiences, tools and promising preliminary ideas. However, papers dealing with synergistic approaches that integrate privacy requirements and protection in personal data analytics are especially welcome.
Authors are invited to submit original research or position papers proposing novel methods or analyzing existing techniques on novel datasets on any relevant topic. These can either be normal or short papers. Short papers can discuss new ideas which are at an early stage of development and which have not yet been thoroughly evaluated. Topics of interest to the workshop include, but are not limited to, the following:
• Personal model summarizing the user's behaviors
• Personal data and knowledge management (databases, software, formats)
• Personal data collection (crawling, storage, compression)
• Personal data integration
• Personal Data Store and Personal Information Management Systems models
• Parameter-free and auto-adaptive methodologies for personal analytics
• Novel indicators measuring personal behavior
• Individual vs. collective models
• Privacy-preserving mining algorithm
• Privacy-preserving individual data sharing
• Privacy risk assessment
• Privacy and anonymity in collective services
• Information (data/patterns) hiding
• Privacy in pervasive/ubiquitous systems
• Security and privacy metrics
• Personal data protection and law enforcement
• Balancing privacy and quality of the service/analysis
• Case study analysis and experiments on real individual data