Regulation (EU) 2016/679 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data (General Data Protection Regulation – GDPR)

Otto, Marta

doi:10.5771/9783845266190-974

Cited by 57 publications

(18 citation statements)

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“…Planning to use smart-meter reading in Spain and Italy in EmpowerMed makes sense considering the latest EU smart metering benchmark assessment [7], as these are two of the few countries that have deployed almost 100% of smart meters among domestic electricity consumers. However, taking a deeper look into the purposes of the ongoing digitalization of electricity consumption metering, one can see that, in the case of Spain, the focus is on digitalizing the "distribution grid and optimize network operations" and for "retail markets to foster innovation and new services by private actors".…”

Section: The Empowermed Project Deployment In Barcelona's Pilot mentioning

confidence: 99%

“…Individual energy consumption data collected through smart meters gives way to advanced data capturing and processing technologies such as big data and block chain enabled by the continuously increasing computing capabilities of IT systems. In the EU, the uptake of smart meters is proceeding unevenly with Denmark, Italy, Spain, Sweden, the UK, France, Netherlands, Austria and Greece likely achieving a wide-scale rollout by 2020, whereas other Member States are only expected to do so after 2030 [7]. This form of 'digital transition' follows an EU mandate according to which at least 80% of electricity meters need to be replaced with smart meters by 2020 "wherever it is cost-effective to do so".…”

Section: Introductionmentioning

confidence: 99%

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Smart Meters Tackling Energy Poverty Mitigation: Uses, Risks and Approaches

Casals

Herrero

Barbero

et al. 2020

2020 IEEE Electric Power and Energy Conference (EPEC)

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Big data analysis is becoming an increasing field of interest for research to analyse, identify and predict final user's behaviour. For this reason, in the energy sector, smart metering is generally used to find new business opportunities and, theoretically, it is said that it could also help to fight energy poverty issues. Nonetheless, when tackling social injustice issues, the deployment of massive technology might also bring other side effects. This study analyses the capability of smart metering to mitigate energy poverty in Europe according to the current approaches of projects and the risks it might bring to the more vulnerable layers of our society.

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Section: The Empowermed Project Deployment In Barcelona's Pilot mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Smart Meters Tackling Energy Poverty Mitigation: Uses, Risks and Approaches

Casals

Herrero

Barbero

et al. 2020

2020 IEEE Electric Power and Energy Conference (EPEC)

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show abstract

“…We estimate 2030 low/high control needs of 9h/7h inter-request time. Further, we suggest that the minimum interrequest time remains constant with a value of 15 minutes since EU regulation on smart metering is planned to require real-time reporting every 15 min (Tractebel, 2019). For handover needs, we assume a 2019 total site density of 48 sites/km2 (Rewheel, 2019).…”

Section: Device Control Needs Forecastmentioning

confidence: 99%

Value network analysis in a low-cost and affordable internet

Benseny

Hämmáinen

2016

2016 European Conference on Networks and Communications (EuCNC)

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“…Meanwhile, it is also highly privacy sensitive since various user profile information such as gender, age, and even political orientation could be inferred from the user behavior data [62]. As a result, the user behavior data is protected with strict department-specific access authorization or regulations such as the General Data Protection Regulation (GDPR) [39].…”

Section: Introductionmentioning

confidence: 99%

On the User Behavior Leakage from Recommender System Exposure

Xin

Yang

Wang

et al. 2023

ACM Trans. Inf. Syst.

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Modern recommender systems are trained to predict users potential future interactions from users historical behavior data. During the interaction process, despite the data coming from the user side recommender systems also generate exposure data to provide users with personalized recommendation slates. Compared with the sparse user behavior data, the system exposure data is much larger in volume since only very few exposed items would be clicked by the user. Besides, the users historical behavior data is privacy sensitive and is commonly protected with careful access authorization. However, the large volume of recommender exposure data which is generated by the service provider itself usually receives less attention and could be accessed within a relatively larger scope of various information seekers or even potential adversaries. In this paper, we investigate the problem of user behavior leakage in the field of recommender systems. We show that the privacy sensitive user past behavior data can be inferred through the modeling of system exposure. In other words, one can infer which items the user have clicked just from the observation of current system exposure for this user . Given the fact that system exposure data could be widely accessed from a relatively larger scope, we believe that the user past behavior privacy has a high risk of leakage in recommender systems. More precisely, we conduct an attack model whose input is the current recommended item slate (i.e., system exposure) for the user while the output is the user’s historical behavior. Specifically, we exploit an encoder-decoder structure to construct the attack model and apply different encoding and decoding strategies to verify the attack performance. Experimental results on two real-world datasets indicate a great danger of user behavior leakage. To address the risk, we propose a two-stage privacy-protection mechanism which firstly selects a subset of items from the exposure slate and then replaces the selected items with uniform or popularity-based exposure. Experimental evaluation reveals a trade-off effect between the recommendation accuracy and the privacy disclosure risk, which is an interesting and important topic for privacy concerns in recommender systems.

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Regulation (EU) 2016/679 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data (General Data Protection Regulation – GDPR)

Cited by 57 publications

References 0 publications

Smart Meters Tackling Energy Poverty Mitigation: Uses, Risks and Approaches

Smart Meters Tackling Energy Poverty Mitigation: Uses, Risks and Approaches

Value network analysis in a low-cost and affordable internet

On the User Behavior Leakage from Recommender System Exposure

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