CREATE Principal Investigators Series: Dr David Eckhoff on Privacy in the Smart City

Privacy in the Smart City: Trade-off or Opportunity 07.06.2019

 

Privacy has been an increasing concern as technology advances. A large amount of personal data is gathered as better technologies are created for more convenience. In the context of smart cities, many application areas and technologies are needed to implement complex interactions between citizens, third parties, and city departments. This overwhelming complexity is one reason why holistic privacy protection rarely enters the picture. A lack of privacy can result in discrimination and social sorting, creating a fundamentally unequal society. As such, this begs the question of how we can safeguard our privacy as we move toward a smart nation.

 

TUMCREATE Principal Investigator (PI), Dr. David Eckhoff, was invited to speak at the monthly CREATE PI Seminar to share more on the topic – Privacy in the Smart City: Trade-off or Opportunity.

 

Dr. David Eckhoff presented the various privacy challenges that both data collectors and individuals face today such as the increase of privacy protection, which could reduce data utility for researchers, and scenarios when individuals would lose their rights to their own data. To further emphasize the importance of privacy, the risks of data publishing were highlighted. He showed the ways actual personal data could be obtained through data publishing, even with anonymization, which proved the danger of how the seemingly harmless, published data could be used against an individual.

 

So, how can we continue to enjoy the convenience of new and improved technologies for a smart city without compromising our privacy wholesale? Dr. David Eckhoff went on to cite two possible ways in which an individual’s data can be protected – by enabling plausible deniability through differential privacy and by the use of synthetic datasets.

 

He explained that by enabling plausible deniability with the addition of ‘noise’ to the reported data, it could make sure that it would not be possible to tell whether a certain individual was included in a dataset. The second approach, synthetic datasets, would rid the use of real data by using completely artificial data, potentially protecting the identity of any real individual. 

 

Furthermore, he mentioned that to overcome the limitations of the two approaches alone, they could be combined to create Differentially Private Synthetic Datasets. Also, the team is currently working on perfecting and applying this method in the context of the Singapore Transport System and the result of this research can promise an effective way to safeguard a person’s privacy while providing useful datasets for analysis.

 

The presentation was then concluded with a Q&A session with interests raised from the audience on the applicability of differential privacy and perceived privacy.

---

About Dr David Eckhoff

Dr David Eckhoff is the principal investigator of the computer science group at TUMCREATE Singapore. David received his Ph.D. degree in engineering (Dr.-Ing., nominated for best PhD thesis in Germany 2016) and his M.Sc. degree in computer science (Dipl.-Inf. Univ., graduating top of his class) from the University of Erlangen-Nuremberg in Germany. In 2016 he was a visiting scholar with the group of Prof. Lars Kulik at the University of Melbourne, Australia. In October 2016, he joined TUMCREATE in Singapore. His research interests include privacy protection, smart cities, vehicular networks, and intelligent transportation systems with a focus on modelling and simulation.