PRESERVATION
Algorithm 6.1: Distributed Coloring Require: INPUT
7.6 Concluding Remarks and Future Research
In this chapter, we presented the privacy issues caused by various IoT devices used in smart buildings; in particular, to support energy-efficient and environ- mentally friendly building services for occupants’ comfort. We identified three privacy issues that have to be solved to realize privacy-aware smart buildings:
user behavior privacy, which can be inferred from fine-grained meter read- ings or by tracking mobile IoT devices, location privacy, and visual privacy.
We surveyed several privacy-preserving approaches for identified IoT devices causing privacy concern, categorized the approaches, and provided an overview of them. We also provided references to other useful resources for interested readers.
We also discussed that the privacy issue with the smart meter arises from the disaggregation of the power consumption to obtain appliance-level information by using NILM approaches. This is not the only way to obtain this information.
As a matter of fact, a remote monitoring service has been offered for several years for online monitoring of and possible online services for HVAC systems.
This remote service enables a third party to access the device and collect some operational information from it for accurate fault detection and suitable proposed corrective actions. Collecting data remotely may also reveal some occupancy information, such as when the occupant is in the building or not. When this remote monitoring service is widely be adopted for various smart appliances in the near future, the similar issue will arise. The third party will have access to usage reports of each smart appliance for diagnostics and repairs. Therefore, privacy-aware remote monitoring services may become one of the future research directions.
Another possible research direction is to involve interdisciplinary research and incorporate the user perspective into privacy research. Most of the approaches are based on fixed assumptions about the user’s privacy perspectives.
However, each user may have a different sensitivity to privacy, which needs to be reflected in the various approaches. This requires ethnographic approaches
by social scientists to understand the needs of the users. Once those needs are identified, differential privacy can be offered via novel approaches.
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MixGroup constructs extended pseudonym-changing regions, in which vehicles are allowed to successively exchange their pseudonyms. As a consequence, for the tracking adversary, the uncertainty of the pseudonym mixture is cumulatively enlarged, and therefore location privacy preservation is considerably improved.
We carry out simulations to verify the performance of MixGroup. Results indi- cate that MixGroup significantly outperforms the existing schemes. In addi- tion, MixGroup is able to achieve a favorable performance even in low traffic conditions.
Keywords: Location privacy, Internet of Vehicles, vehicular social network, pseudonym, group signature