Bringing transparency and compliance verification to the online data ecosystem
what is privacy auditing?
Privacy auditing refers to the process of evaluating and assessing an organization's practices, procedures,
and systems to ensure compliance with privacy laws, regulations, and industry standards. It involves a systematic
examination of an organization's privacy policies, data handling practices, security measures, and data protection
controls to identify any gaps, vulnerabilities, or areas of non-compliance related to privacy. The goal is to
ensure that personal data is collected, used, stored, and shared in a manner that respects individuals' privacy
rights and maintains the confidentiality, integrity, and availability of the data.
At the SPARTA lab, we're interested in developing methodologies and policy recommendations to enable to
automated detection of non-compliance with privacy regulations. The research challenge is to satisfy the
need for high-quality audits while being denied access to the internals of the systems that are the focus of
the audits.
@article{Oakland-2024,title={{The Inventory is Dark and Full of Misinformation: Understanding
the Abuse of Ad Inventory Pooling in the Ad-Tech Supply Chain}},author={Vekaria, Yash and Nithyanand, Rishab and Shafiq, Zubair},journal={IEEE Symposium on Security \& Privacy},year={2024},}
Forms of Disclosure: The Path to Automated Data Privacy Audits
Mihailis Diamantis, Maaz Bin Musa, Lucas Ausberger, and Rishab Nithyanand
Harvard Journal of Law & Technology (Forthcoming), 2023
@article{JOLT-2023,title={{Forms of Disclosure: The Path to Automated Data Privacy Audits}},author={Diamantis, Mihailis and Musa, Maaz Bin and Ausberger, Lucas and Nithyanand, Rishab},journal={Harvard Journal of Law \& Technology (Forthcoming)},volume={37},number={3},year={2023},}
ATOM: A Generalizable Technique for Inferring Tracker-Advertiser Data Sharing in the Online Behavioral Advertising Ecosystem
@article{pets-2022,title={ATOM: A Generalizable Technique for Inferring Tracker-Advertiser Data Sharing in the Online Behavioral Advertising Ecosystem},author={Musa, Maaz Bin and Nithyanand, Rishab},journal={Privacy Enhancing Technologies Symposium (PETS)},year={2022},award={Andreas Pfitzmann Award Runner-up},}
Inferring Tracker-Advertiser Relationships in the Online Advertising Ecosystem using Header Bidding
@article{Cook-PETS2020,author={Cook, John and Nithyanand, Rishab and Shafiq, Zubair},journal={Privacy Enhancing Technologies Symposium (PETS)},title={Inferring Tracker-Advertiser Relationships in the Online Advertising Ecosystem using Header Bidding},year={2020},volume={abs/1907.07275},}
Apps, Trackers, Privacy, and Regulators: A Global Study of the Mobile
Tracking Ecosystem
Abbas Razaghpanah, Rishab Nithyanand, Narseo Vallina-Rodriguez, Srikanth Sundaresan, Mark Allman, Christian Kreibich, and Phillipa Gill
Network and Distributed System Security Symposium (NDSS), 2018
@article{DBLP:conf/ndss/RazaghpanahNVSA18,author={Razaghpanah, Abbas and Nithyanand, Rishab and Vallina{-}Rodriguez, Narseo and Sundaresan, Srikanth and Allman, Mark and Kreibich, Christian and Gill, Phillipa},title={Apps, Trackers, Privacy, and Regulators: {A} Global Study of the Mobile
Tracking Ecosystem},journal={Network and Distributed System Security Symposium (NDSS)},year={2018},}
Adblocking and Counter Blocking: A Slice of the Arms Race
Rishab Nithyanand, Sheharbano Khattak, Mobin Javed, Narseo Vallina-Rodriguez, Marjan Falahrastegar, Julia E. Powles, Emiliano De Cristofaro, Hamed Haddadi, and Steven J. Murdoch
6th USENIX Workshop on Free and Open Communications on the
Internet (FOCI), 2016
@article{DBLP:conf/uss/NithyanandKJVFP16,author={Nithyanand, Rishab and Khattak, Sheharbano and Javed, Mobin and Vallina{-}Rodriguez, Narseo and Falahrastegar, Marjan and Powles, Julia E. and Cristofaro, Emiliano De and Haddadi, Hamed and Murdoch, Steven J.},title={Adblocking and Counter Blocking: {A} Slice of the Arms Race},journal={6th {USENIX} Workshop on Free and Open Communications on the
Internet (FOCI)},year={2016},crossref={DBLP:conf/uss/2016foci},}
