相关论文: Mapping the Privacy-Utility Tradeoff in Mobile Pho…
Prior approaches for membership privacy preservation usually update or retrain all weights in neural networks, which is costly and can lead to unnecessary utility loss or even more serious misalignment in predictions between training data…
Secondary use of data already collected in clinical studies has become more and more popular in recent years, with the commitment of the pharmaceutical industry and many academic institutions in Europe and the US to provide access to their…
Human mobility data is a crucial resource for urban mobility management, but it does not come without personal reference. The implementation of security measures such as anonymization is thus needed to protect individuals' privacy. Often, a…
In sensitive applications involving relational datasets, protecting information about individual links from adversarial queries is of paramount importance. In many such settings, the available data are summarized solely through the degrees…
The exposure of location data constitutes a significant privacy risk to users as it can lead to de-anonymization, the inference of sensitive information, and even physical threats. In this paper we present LPAuditor, a tool that conducts a…
With the aim of informing sound policy about data sharing and privacy, we describe successful re-identification of patients in an Australian de-identified open health dataset. As in prior studies of similar datasets, a few mundane facts…
Mobile phone metadata is increasingly used for humanitarian purposes in developing countries as traditional data is scarce. Basic demographic information is however often absent from mobile phone datasets, limiting the operational impact of…
Organizations that collect and analyze data may wish or be mandated by regulation to justify and explain their analysis results. At the same time, the logic that they have followed to analyze the data, i.e., their queries, may be…
A privacy-utility tradeoff is developed for an arbitrary set of finite-alphabet source distributions. Privacy is quantified using differential privacy (DP), and utility is quantified using expected Hamming distortion maximized over the set…
A mechanism for releasing information about a statistical database with sensitive data must resolve a trade-off between utility and privacy. Privacy can be rigorously quantified using the framework of {\em differential privacy}, which…
Data engineering often requires accuracy (utility) constraints on results, posing significant challenges in designing differentially private (DP) mechanisms, particularly under stringent privacy parameter $\epsilon$. In this paper, we…
The risks of publishing privacy-sensitive data have received considerable attention recently. Several de-anonymization attacks have been proposed to re-identify individuals even if data anonymization techniques were applied. However, there…
Being able to release and exploit open data gathered in information systems is crucial for researchers, enterprises and the overall society. Yet, these data must be anonymized before release to protect the privacy of the subjects to whom…
The digital era has raised many societal challenges, including ICT's rising energy consumption and protecting privacy of personal data processing. This paper considers both aspects in relation to machine learning accuracy in an…
With the ubiquitous use of location-based services, large-scale individual-level location data has been widely collected through location-awareness devices. The widespread exposure of such location data poses significant privacy risks to…
Mobility data is essential for cities and communities to identify areas for necessary improvement. Data collected by mobility providers already contains all the information necessary, but privacy of the individuals needs to be preserved.…
In the context of a myriad of mobile apps which collect personally identifiable information (PII) and a prospective market place of personal data, we investigate a user-centric monetary valuation of mobile PII. During a 6-week long user…
The design of privacy mechanisms for two scenarios is studied where the private data is hidden or observable. In the first scenario, an agent observes useful data $Y$, which is correlated with private data $X$, and wants to disclose the…
Through seven publications this dissertation shows how anonymized mobile phone data can contribute to the social good and provide insights into human behaviour on a large scale. The size of the datasets analysed ranges from 500 million to…
The problem of private information "leakage" (inadvertently or by malicious design) from the myriad large centralized searchable data repositories drives the need for an analytical framework that quantifies unequivocally how safe private…