11,285 research outputs found
Privacy risks in trajectory data publishing: reconstructing private trajectories from continuous properties
Location and time information about individuals can be captured through GPS devices, GSM phones, RFID tag readers, and by other similar means. Such data can be pre-processed to obtain trajectories which are sequences of spatio-temporal data points belonging to a moving object. Recently, advanced data mining techniques have been developed for extracting patterns from moving object trajectories to enable applications such as city traffic planning, identification of evacuation routes, trend detection, and many more. However, when special care is not taken, trajectories of individuals may also pose serious privacy risks even after they are de-identified or mapped into other forms. In this paper, we show that an unknown private trajectory can be reconstructed from knowledge of its properties released for data mining, which at first glance may not seem to pose any privacy threats. In particular, we propose a technique to demonstrate how private trajectories can be re-constructed from knowledge of their distances to a bounded set of known trajectories. Experiments performed on real data sets show that the number of known samples is surprisingly smaller than the actual theoretical bounds
Discovering private trajectories using background information
Trajectories are spatio-temporal traces of moving objects which contain valuable information to be harvested by spatio-temporal data mining techniques. Applications like city traffic planning, identification of evacuation routes, trend detection, and many more can benefit from trajectory mining. However, the trajectories of individuals often contain private and sensitive information, so anyone who possess trajectory data must take special care when disclosing this data. Removing identifiers from trajectories before the release is not effective against linkage type attacks, and rich sources of background information make it even worse. An alternative is to apply transformation techniques to map the given set of trajectories into another set where the distances are preserved. This way, the actual trajectories are not released, but the distance information can still be used for data mining techniques such as clustering. In this paper, we show that an unknown private trajectory can be reconstructed using the available background information together with the mutual distances released for data mining purposes. The background knowledge is in the form of known trajectories and extra information such as the speed limit. We provide analytical results which bound the number of the known trajectories needed to reconstruct private trajectories. Experiments performed on real trajectory data sets show that the number of known samples is surprisingly smaller than the actual theoretical bounds
Privacy via the Johnson-Lindenstrauss Transform
Suppose that party A collects private information about its users, where each
user's data is represented as a bit vector. Suppose that party B has a
proprietary data mining algorithm that requires estimating the distance between
users, such as clustering or nearest neighbors. We ask if it is possible for
party A to publish some information about each user so that B can estimate the
distance between users without being able to infer any private bit of a user.
Our method involves projecting each user's representation into a random,
lower-dimensional space via a sparse Johnson-Lindenstrauss transform and then
adding Gaussian noise to each entry of the lower-dimensional representation. We
show that the method preserves differential privacy---where the more privacy is
desired, the larger the variance of the Gaussian noise. Further, we show how to
approximate the true distances between users via only the lower-dimensional,
perturbed data. Finally, we consider other perturbation methods such as
randomized response and draw comparisons to sketch-based methods. While the
goal of releasing user-specific data to third parties is more broad than
preserving distances, this work shows that distance computations with privacy
is an achievable goal.Comment: 24 page
Synthetic Data Generation Using Wasserstein Conditional Gans With Gradient Penalty (WCGANS-GP)
With data protection requirements becoming stricter, the data privacy has become increasingly important and more crucial than ever. This has led to restrictions on the availability and dissemination of real-world datasets. Synthetic data offers a viable solution to overcome barriers of data access and sharing. Existing data generation methods require a great deal of user-defined rules, manual interactions and domainspecific knowledge. Moreover, they are not able to balance the trade-off between datausability and privacy. Deep learning based methods like GANs have seen remarkable success in synthesizing images by automatically learning the complicated distributions and patterns of real data. But they often suffer from instability during the training process
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Habitual Disclosure: Routine, Affordance and the Ethics of Young Peoples Social Media Data Surveillance
Drawing on findings from qualitative interviews and photo elicitation, this paper explores young people’s experiences of breaches of trust with social media platforms and how comfort is re-established despite continual violations. It provides rich qualitative accounts of users habitual relations with social media platforms. In particular, we seek to trace the process by which online affordances create conditions in which ‘sharing’ is regarded as not only routine and benign but pleasurable. Rather it is the withholding of data that is abnormalised. This process has significant implications for the ethics of data collection by problematising a focus on ‘consent’ to data collection by social media platforms. Active engagement with social media, we argue, is premised on a tentative, temporary, shaky trust that is repeatedly ruptured and repaired. We seek to understand the process by which violations of privacy and trust in social media platforms are remediated by their users and rendered ordinary again through everyday habits. We argue that the processes by which users become comfortable with social media platforms, through these routines, calls for an urgent reimagining of data privacy beyond the limited terms of consent
PRUDEnce: A system for assessing privacy risk vs utility in data sharing ecosystems
Data describing human activities are an important source of knowledge useful for understanding individual and collective behavior and for developing a wide range of user services. Unfortunately, this kind of data is sensitive, because people’s whereabouts may allow re-identification of individuals in a de-identified database. Therefore, Data Providers, before sharing those data, must apply any sort of anonymization to lower the privacy risks, but they must be aware and capable of controlling also the data quality, since these two factors are often a trade-off. In this paper we propose PRUDEnce (Privacy Risk versus Utility in Data sharing Ecosystems), a system enabling a privacy-aware ecosystem for sharing personal data. It is based on a methodology for assessing both the empirical (not theoretical) privacy risk associated to users represented in the data, and the data quality guaranteed only with users not at risk. Our proposal is able to support the Data Provider in the exploration of a repertoire of possible data transformations with the aim of selecting one specific transformation that yields an adequate trade-off between data quality and privacy risk. We study the practical effectiveness of our proposal over three data formats underlying many services, defined on real mobility data, i.e., presence data, trajectory data and road segment data
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