This paper presents an eHealth use case based on a privacy-preserving machine learning platform to detect arrhythmia developed by the PAPAYA project that can run in an untrusted domain. It discusses legal privacy and user requirements that we elicited for this use case from the GDPR and via stakeholder interviews. These include requirements for secure pseudonymisation schemes, for allowing also pseudonymous users to exercise their data subjects rights, for not making diagnostic decisions fully automatically and for assurance guarantees, conformance with specified standards and informing clinicians and patients about the privacy protection. The requirements are not only relevant for our use case but also for other use cases utilising privacy-preserving data analytics to classify medical data.

Privacy is a highly complex subject, especially when it comes to balancing data subjects' expectations, requirements and needs with i) the objectives of service providers and data controllers, and ii) the variety of legal obligations that dictate protection rights of data subjects and responsibilities of data controllers. This requires to provide technical solutions capable of matching different and adequate levels of privacy, while still attending to data subjects' preferences and business objectives. The Data Protection Orchestrator (DPO) developed in the context of the WITDOM project1 meets this challenge by interacting with different Protection Enhancing Technologies or Services following a set of pre-defined protection processes, so as to support automated management trade-offs between privacy, performance and utility. By leveraging Business Process Management standards, the DPO is capable of making data protection processes and practices (such as automated anonymization or management of data subject's consent) integral to other business core services, as intended with the data protection by design and by default approach in the EU's GDPR. The DPO capabilities will be explained in the context of two complementary scenarios: the eHealth scenarios, where the DPO will be used for protecting genomic data and the financial scenario where the DPO will be responsible for protecting the transaction history and personal attributes of the bank's customers.

Both social media and sensing infrastructures are producing an unprecedented mass of data characterized by their uncertain nature, due to either the noise inherent in sensors or the imprecision of human contributions. Therefore query processing over uncertain data has become an active research field. In the well-known class of applications commonly referred to as “top-K queries”, the objective is to find the best K objects matching the user's information need, formulated as a scoring function over the objects' attribute values. If both the data and the scoring function are deterministic, the best K objects can be univocally determined and totally ordered so as to produce a single ranked result set (as long as ties are broken by some deterministic rule). However, in application scenarios involving uncertain data and fuzzy information needs, this does not hold: when either the attribute values or the scoring function are nondeterministic, there may be no consensus on a single ordering, but rather a space of possible orderings. To determine the correct ordering, one needs to acquire additional information so as to reduce the amount of uncertainty associated with the queried data and consequently the number of orderings in such a space. An emerging trend in data processing is crowdsourcing, defined as the systematic engagement of humans in the resolution of tasks through online distributed work. Our approach combines human and automatic computation in order to solve complex problems: when data ambiguity can be resolved by human judgment, crowdsourcing becomes a viable tool for converging towards a unique or at least less uncertain query result. The goal of this paper is to define and compare task selection policies for uncertainty reduction via crowdsourcing, with emphasis on the case of top-K queries.

Querying uncertain data has become a prominent application due to the proliferation of user-generated content from social media and of data streams from sensors. When data ambiguity cannot be reduced algorithmically, crowdsourcing proves a viable approach, which consists of posting tasks to humans and harnessing their judgment for improving the confidence about data values or relationships. This paper tackles the problem of processing top-K queries over uncertain data with the help of crowdsourcing for quickly converging to the realordering of relevant results. Several offline and online approaches for addressing questions to a crowd are defined and contrasted on both synthetic and real data sets, with the aim of minimizing the crowd interactions necessary to find the realordering of the result set.

Top-k diversity queries over objects embedded in a low-dimensional vector space aim to retrieve the best k objects that are both relevant to given user's criteria and well distributed over a designated region. An interesting case is provided by spatial Web objects, which are produced in great quantity by location-based services that let users attach content to places and are found also in domains like trip planning, news analysis, and real estate. In this article we present a technique for addressing such queries that, unlike existing methods for diversified top- k queries, does not require accessing and scanning all relevant objects in order to find the best k results. Our Space Partitioning and Probing (SPP) algorithm works by progressively exploring the vector space, while keeping track of the already seen objects and of their relevance and position. The goal is to provide a good quality result set in terms of both relevance and diversity. We assess quality by using as a baseline the result set computed by MMR, one of the most popular diversification algorithms, while minimizing the number of accessed objects. In order to do so, SPP exploits score-based and distance-based access methods, which are available, for instance, in most geo-referenced Web data sources. Experiments with both synthetic and real data show that SPP produces results that are relevant and spatially well distributed, while significantly reducing the number of accessed objects and incurring a very low computational overhead.