19,281 research outputs found
DP-Image: Differential Privacy for Image Data in Feature Space
The excessive use of images in social networks, government databases, and
industrial applications has posed great privacy risks and raised serious
concerns from the public. Even though differential privacy (DP) is a widely
accepted criterion that can provide a provable privacy guarantee, the
application of DP on unstructured data such as images is not trivial due to the
lack of a clear qualification on the meaningful difference between any two
images. In this paper, for the first time, we introduce a novel notion of
image-aware differential privacy, referred to as DP-image, that can protect
user's personal information in images, from both human and AI adversaries. The
DP-Image definition is formulated as an extended version of traditional
differential privacy, considering the distance measurements between feature
space vectors of images. Then we propose a mechanism to achieve DP-Image by
adding noise to an image feature vector. Finally, we conduct experiments with a
case study on face image privacy. Our results show that the proposed DP-Image
method provides excellent DP protection on images, with a controllable
distortion to faces
Are You in the Line? RSSI-based Queue Detection in Crowds
Crowd behaviour analytics focuses on behavioural characteristics of groups of
people instead of individuals' activities. This work considers human queuing
behaviour which is a specific crowd behavior of groups. We design a
plug-and-play system solution to the queue detection problem based on
Wi-Fi/Bluetooth Low Energy (BLE) received signal strength indicators (RSSIs)
captured by multiple signal sniffers. The goal of this work is to determine if
a device is in the queue based on only RSSIs. The key idea is to extract
features not only from individual device's data but also mobility similarity
between data from multiple devices and mobility correlation observed by
multiple sniffers. Thus, we propose single-device feature extraction,
cross-device feature extraction, and cross-sniffer feature extraction for model
training and classification. We systematically conduct experiments with
simulated queue movements to study the detection accuracy. Finally, we compare
our signal-based approach against camera-based face detection approach in a
real-world social event with a real human queue. The experimental results
indicate that our approach can reach minimum accuracy of 77% and it
significantly outperforms the camera-based face detection because people block
each other's visibility whereas wireless signals can be detected without
blocking.Comment: This work has been partially funded by the European Union's Horizon
2020 research and innovation programme within the project "Worldwide
Interoperability for SEmantics IoT" under grant agreement Number 72315
Statistical Watermarking for Networked Control Systems
Watermarking can detect sensor attacks in control systems by injecting a
private signal into the control, whereby attacks are identified by checking the
statistics of the sensor measurements and private signal. However, past
approaches assume full state measurements or a centralized controller, which is
not found in networked LTI systems with subcontrollers. Since generally the
entire system is neither controllable nor observable by a single subcontroller,
communication of sensor measurements is required to ensure closed-loop
stability. The possibility of attacking the communication channel has not been
explicitly considered by previous watermarking schemes, and requires a new
design. In this paper, we derive a statistical watermarking test that can
detect both sensor and communication attacks. A unique (compared to the
non-networked case) aspect of the implementing this test is the state-feedback
controller must be designed so that the closed-loop system is controllable by
each sub-controller, and we provide two approaches to design such a controller
using Heymann's lemma and a multi-input generalization of Heymann's lemma. The
usefulness of our approach is demonstrated with a simulation of detecting
attacks in a platoon of autonomous vehicles. Our test allows each vehicle to
independently detect attacks on both the communication channel between vehicles
and on the sensor measurements
[How] Can Pluralist Approaches to Computational Cognitive Modeling of Human Needs and Values Save our Democracies?
In our increasingly digital societies, many companies have business models that perceive users’ (or customers’) personal data as a siloed resource, owned and controlled by the data controller rather than the data subjects. Collecting and processing such a massive amount of personal data could have many negative technical, social and economic consequences, including invading people’s privacy and autonomy. As a result, regulations such as the European General Data Protection Regulation (GDPR) have tried to take steps towards a better implementation of the right to digital privacy. This paper proposes that such legal acts should be accompanied by the development of complementary technical solutions such as Cognitive Personal Assistant Systems to support people to effectively manage their personal data processing on the Internet. Considering the importance and sensitivity of personal data processing, such assistant systems should not only consider their owner’s needs and values, but also be transparent, accountable and controllable. Pluralist approaches in computational cognitive modelling of human needs and values which are not bound to traditional paradigmatic borders such as cognitivism, connectionism, or enactivism, we argue, can create a balance between practicality and usefulness, on the one hand, and transparency, accountability, and controllability, on the other, while supporting and empowering humans in the digital world. Considering the threat to digital privacy as significant to contemporary democracies, the future implementation of such pluralist models could contribute to power-balance, fairness and inclusion in our societies
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