openPDS: Protecting the Privacy of Metadata through SafeAnswers

Abstract

The rise of smartphones and web services made possible the large-scale collection of personal metadata. Information about individuals' location, phone call logs, or web-searches, is collected and used intensively by organizations and big data researchers. Metadata has however yet to realize its full potential. Privacy and legal concerns, as well as the lack of technical solutions for personal metadata management is preventing metadata from being shared and reconciled under the control of the individual. This lack of access and control is furthermore fueling growing concerns, as it prevents individuals from understanding and managing the risks associated with the collection and use of their data. Our contribution is two-fold: (1) we describe openPDS, a personal metadata management framework that allows individuals to collect, store, and give fine-grained access to their metadata to third parties. It has been implemented in two field studies; (2) we introduce and analyze SafeAnswers, a new and practical way of protecting the privacy of metadata at an individual level. SafeAnswers turns a hard anonymization problem into a more tractable security one. It allows services to ask questions whose answers are calculated against the metadata instead of trying to anonymize individuals' metadata. The dimensionality of the data shared with the services is reduced from high-dimensional metadata to low-dimensional answers that are less likely to be re-identifiable and to contain sensitive information. These answers can then be directly shared individually or in aggregate. openPDS and SafeAnswers provide a new way of dynamically protecting personal metadata, thereby supporting the creation of smart data-driven services and data science research.

Building accountability into the Internet of Things: the IoT Databox model

“…OpenPDS [6] is hosted on either a smartphone or an internet-connected hard drive situated in the home. OpenPDS provides users with a centralized location for storing personal data and exploits the 'SafeAnswers' approach [7] to compute third-party queries inside a software sandbox within the user's PDS returning, like the IoT Databox, only the results of processing not the raw data. HAT [36] provides users with a personal container that also stores data client-side.…”

Personal Analytics and Privacy. An Individual and Collective Perspective

“…Although some user-centric models like the Personal Intelligent Management Systems (PIMS) and the Personal Data Store (PDS) are emerging [1,5], 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.…”

“…Figure 1(a) shows the overall Personal Data Analytics approach. The individual data flow into the PDS and are stored according to one of the possible technique described in the PDS literature [1,4,5]. Along the analysis of the continuous digital breadcrumbs, the PDS must consider that it does not exist a unique and constant model describing human behaviors.…”

Market Basket Prediction Using User-Centric Temporal Annotated Recurring Sequences

“…This aspect eases the customers' personal data management and allows for developing tailored basket recommenders that can even run on the customers' mobile devices [6,29].…”