Towards Privacy-Aware Smart Buildings: Capturing, Communicating, and Enforcing Privacy Policies and Preferences

Pappachan, Primal; Degeling, Martin; Yus, Roberto; Das, Anupam; Bhagavatula, Sruti; Melicher, William; Naeini, Pardis Emami; Zhang, Shikun; Bauer, Lujo; Kobsa, Alfred; Mehrotra, Sharad; Sadeh, Norman; Venkatasubramanian, Nalini

doi:10.1109/icdcsw.2017.52

Cited by 49 publications

(23 citation statements)

References 11 publications

(16 reference statements)

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“…This is very similar to the use of HVAC systems above as they are all thermal energy storage units. These works can be used to complement privacy‐preserving policies in smart buildings [101–103].…”

Section: Smart‐meter Privacy With Demand Shaping and Load Schedulingmentioning

confidence: 99%

Review of results on smart‐meter privacy by data manipulation, demand shaping, and load scheduling

Farokhi

2020

IET Smart Grid

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Section: Smart‐meter Privacy With Demand Shaping and Load Schedulingmentioning

confidence: 99%

Review of results on smart‐meter privacy by data manipulation, demand shaping, and load scheduling

Farokhi

2020

IET Smart Grid

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“…Liu et al 2016) or smart buildings (e.g. Pappachan et al 2017). However, none of these studies focuses on the user interface to control or understand privacy settings.…”

Section: Users' Privacy Perceptions and Privacy Assistancementioning

confidence: 99%

User preferences for privacy features in digital assistants

et al. 2020

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Digital assistants (DA) perform routine tasks for users by interacting with the Internet of Things (IoT) devices and digital services. To do so, such assistants rely heavily on personal data, e.g. to provide personalized responses. This leads to privacy concerns for users and makes privacy features an important component of digital assistants.This study examines user preferences for three attributes of the design of privacy features in digital assistants, namely (1) the amount of information on personal data that is shown to the user, (2) explainability of the DA’s decision, and (3) the degree of gamification of the user interface (UI). In addition, it estimates users’ willingness to pay (WTP) for different versions of privacy features.The results for the full sample show that users prefer to understand the rationale behind the DA’s decisions based on the personal information involved, while being given information about the potential impacts of disclosing specific data. Further, the results indicate that users prefer to interact with the DA’s privacy features in a serious game. For this product, users are willing to pay €21.39 per month. In general, a playful design of privacy features is strongly preferred, as users are willing to pay 23.8% more compared to an option without any gamified elements. A detailed analysis identifies two customer clusters “Best Agers” and “DA Advocates”, which differ mainly in their average age and willingness to pay. Further, “DA Advocates” are mainly male and more privacy sensitive, whereas “Best Agers” show a higher affinity for a playful design of privacy features.

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“…In view of this problem, many scholars have done many researches in recent years. Reference [12] is based on active learning, users need to mark some selected data before the data are classified by machine learning. Some test platforms [13] obtain the original data from different sensors in the building, processing high-level semantic information from these data, and is able to capture and enhance the privacy preferences expressed by the residents of the building.…”

Section: Relative Workmentioning

confidence: 99%

Preference Preserved Privacy Protection Scheme for Smart Home Network System Based on Information Hiding

2020

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Smart home is an emerging form of the Internet of things (IoT), which provides a convenient and comfortable living environment for the smart home users. With the explosive growth of smart home information, people pay more attention to the privacy protection of smart home, including the choice of privacy and effective privacy protection scheme. This paper proposes a privacy protection scheme based on information hiding, the scheme guarantees the sensitive data transmitted securely. First, the smart home Real-time sensor data are classified into sensitive data and non-sensitive data through machine learning, the process can be controlled according to the user's preferences. Second, the sensitive data can be transmitted securely by ordinary channels using method of combination encryption with information hiding. Experiment results show that this scheme can help users to express their preferences to the privacy protection, changing the previous practice that users can only accept the privacy settings provided by the supplier. What's more, the scheme combines encryption with information hiding, providing a double guarantee for the sensitive data during they are transmitted on ordinary channels. Comparing to the previous practice of using encryption merely, this scheme greatly improves the security of data transmission and is more suitable for the real environment. INDEX TERMS Smart home, privacy protection, data encryption, information hiding.

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Towards Privacy-Aware Smart Buildings: Capturing, Communicating, and Enforcing Privacy Policies and Preferences

Abstract: County (UMBC)ScholarWorks@UMBC digital repository on the Maryland Shared Open Access (MD-SOAR) platform.

Cited by 49 publications

References 11 publications

Review of results on smart‐meter privacy by data manipulation, demand shaping, and load scheduling

Review of results on smart‐meter privacy by data manipulation, demand shaping, and load scheduling

User preferences for privacy features in digital assistants

Preference Preserved Privacy Protection Scheme for Smart Home Network System Based on Information Hiding

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