![](bg8.png)
fun, etc.. On the contrary, the categorization of
sites in GRISEO is based on trust: GRISEO asks
users a simple question: do you trust this site
enough so as to white-list it?
• In Firefox, web sites are assigned to containers
and do not change unless the user takes some ac-
tion. On the contrary, in GRISEO, white-listed
web sites may automatically temporarily become
black-listed as shown in figure 2, when included
in a broader “umbrella” of a black-listed web site.
To summarize, we believe that our approach is or-
thogonal and complementary to previous work, as it
builds on top of the two existing browsing modes that
most browsers provide: incognito and regular.
6 CONCLUSION
Over the past few years browsers have started to
provide a “privacy-friendly” browsing mode, called
Incognito or Private mode. Although this mode is
very useful, users many times frequently find them-
selves having the need to switch from Incognito to
regular mode and vice versa, in order to log in into
their accounts in several web sites (such as banks,
email providers, social networks, etc.).
We believe that frequently switching back and
forth between regular and Incognito browsing mode
is tedious and error-prone. For this reason we have
developed GRISEO: an approach that mixes the ad-
vantages of both worlds. It enables users to preserve
their privacy by browsing in Incognito mode but also
maintain the user experience of a regular mode when
they want to visit a website that they trust, which re-
quires from them to log in.
Our preliminary results show that GRISEO can be
implemented in 600 lines of code. In addition, we
see that the imposed overhead is practically negligible
since for the average web page access, the rendering
time while browsing in GRISEO mode is just 5.9%
more than while in Chrome’s Regular browsing mode.
We believe that approaches like GRISEO, which (i)
reduce tracking, (ii) are simple to use, and (iii) do not
affect functionality will be increasingly useful in the
near future.
ACKNOWLEDGEMENTS
The research leading to these results has received
funding from the EU H2020-SU-ICT-03-2018 Project
No. 830929 CyberSec4Europe (cybersec4europe.eu).
The paper reflects only the authors’ view and the
Agency and the Commission are not responsible for
any use that may be made of the information it con-
tains.
REFERENCES
Acar, G., Eubank, C., Englehardt, S., Juarez, M.,
Narayanan, A., and Diaz, C. (2014). The web never
forgets: Persistent tracking mechanisms in the wild. In
Proceedings of the 2014 ACM SIGSAC Conference on
Computer and Communications Security, pages 674–
689.
Agarwal, P., Joglekar, S., Papadopoulos, P., Sastry, N., and
Kourtellis, N. (2020). Stop tracking me bro! differen-
tial tracking of user demographics on hyper-partisan
websites.
Bashir, M. A. and Wilson, C. (2018). Diffusion of
user tracking data in the online advertising ecosys-
tem. Proceedings on Privacy Enhancing Technolo-
gies, 2018(4):85–103.
Cao, Y., Li, S., Wijmans, E., et al. (2017). (cross-) browser
fingerprinting via os and hardware level features. In
Proceedings of Network and Distributed System Secu-
rity Symposium, NDSS’17.
Dingledine, R., Mathewson, N., and Syverson, P. (2004).
Tor: The second-generation onion router. Technical
report, Naval Research Lab Washington DC.
Google Developers (2020). Introduction to service
worker. https://developers.google.com/web/ilt/pwa/
introduction-to-service-worker.
Haque, A., Nisa, S. T., Bari, M., Anika, A. N., et al. (2019).
Anonymity network tor and performance analysis of
aranea; an iot based privacy-preserving router. arXiv
preprint arXiv:1906.01276.
Hoang, N. P., Kintis, P., Antonakakis, M., and Polychron-
akis, M. (2018). An empirical study of the i2p
anonymity network and its censorship resistance. In
Proceedings of the Internet Measurement Conference
2018, pages 379–392.
Iqbal, U., Shafiq, Z., and Qian, Z. (2017). The ad wars: ret-
rospective measurement and analysis of anti-adblock
filter lists. In Proceedings of the 2017 Internet Mea-
surement Conference, pages 171–183.
Klein, A. and Pinkas, B. (2019). Dns cache-based user
tracking. In Proceedings of Network and Distributed
System Security Symposium, NDSS’19.
Kornilakis, A., Papadopoulos, P., and Markatos, E. (2019).
Incognitus: Privacy-preserving user interests in online
social networks. In Fournaris, A. P., Lampropoulos,
K., and Mar
´
ın Tordera, E., editors, Information and
Operational Technology Security Systems.
Lashkari, A. H., Seo, A., Gil, G. D., and Ghorbani, A.
(2017). Cic-ab: Online ad blocker for browsers. In
2017 International Carnahan Conference on Security
Technology (ICCST), pages 1–7. IEEE.
Marrington, A., Baggili, I., Al Ismail, T., and Al Kaf, A.
(2012). Portable web browser forensics: A forensic
examination of the privacy benefits of portable web
Beyond Black and White: Combining the Benefits of Regular and Incognito Browsing Modes
199