2023 Proceeding
In an era of increasing reliance on technology, geospatial data plays a crucial role in shaping novel systems that drive decision-making, improve services, and drive innovation in fields ranging from social media and shopping to healthcare and transportation. In particular, the adoption of machine learning and deep learning techniques has heightened the demand for big geospatial data as well as magnified its transformative potential. Nevertheless, the availability and accessibility of this data, coupled with the security considerations surrounding the models that utilize it, have given rise to pressing ethical concerns that warrant our immediate attention. Striking a delicate balance between maximizing data utility and safeguarding individual privacy has become an imperative challenge. The processing of personal data poses inherent risks, including the potential infringement upon privacy rights and the potential for abuse or manipulation. As a result, profound ethical questions emerge, underscoring the interplay between utility and privacy. This workshop aims to bring together researchers and practitioners from diverse fields to delve into the multifaceted dimensions of geospatial data, unravel its potential implications and identify innovative solutions for enabling smart and safe societies.
Proceeding Downloads
Generic Construction of Key-Aggregate Searchable Encryption
A key-aggregate searchable encryption (KASE) scheme is an encryption scheme that enables a user to search plaintexts under their ciphertexts and control access to the ciphertexts for other users. This paper proposes a generic construction of KASE, ...
On the Effects of Filtering Methods on Adversarial Timeseries Data
Adversarial machine learning is very well studied in image classification. On the other hand, other domains such as deep timeseries classification have not received similar levels of attention, leaving them disproportionately vulnerable. Specifically, ...
A Fundamental Model with Stable Interpretability for Traffic Forecasting
Deep learning models have been widely applied in traffic prediction and analysis. Notably, attention-based models like Graph Attention Network (GAT) have brought significant insights and decisionmaking capabilities to traffic managers through their ...
About privacy on smart tachographs: Reconstructing car-driven routes based on speed measurements
The search for a missing person in a recent criminal case raised the question whether it is possible to reconstruct car-driven routes effectively by using data taken from a digital tachograph. Tachographs are mandatory in many vehicles across multiple ...
Privacy-aware Publication of Wi-Fi Sensor Data for Crowd Monitoring and Tourism Analytics
Estimating visitor frequency in urban areas often relies on camera-based solutions or the active participation of individuals using smartphone applications or devices with RFID or Bluetooth technology. This paper presents the results of a preliminary ...
Towards Anonymizing Intermodal Mobility Data for Smart Cities
- Leonie Ackermann,
- Michael Mühlhauser,
- Alexandru Burdusel,
- Michael Federlin,
- Dominik Herrmann,
- Steffen Holly,
- Daniela Nicklas,
- Daniel Wolpert
As cities seek to optimize their resources for a sustainable and livable future, the concept of intermodal mobility has become increasingly important. However, the collection and analysis of intermodal mobility data is complicated by the need for ...
On the Effect of Mixed Intelligence on Gig-based Food Delivery
Given the growth in adoption of gig-based crowdsourcing food delivery platforms, couriers often find themselves forced to work longer hours and on multiple platforms to earn a fair living wage. Although various research efforts have been dedicated to ...
Index Terms
- Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geo-Privacy and Data Utility for Smart Societies
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Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| GeoPrivacy '14 | 8 | 5 | 63% |
| Overall | 8 | 5 | 63% |