25 citations · 34 across the 8 of their papers we have counts for
15 papers
Making the Most of Parallel Composition in Differential Privacy
Josh Smith, Hassan Jameel Asghar, Gianpaolo Gioiosa +3
We show that the `optimal' use of the parallel composition theorem corresponds to finding the size of the largest subset of queries that `overlap' on the data domain, a quantity we…
Sharing in a Trustless World: Privacy-Preserving Data Analytics with Potentially Cheating Participants
Tham Nguyen, Hassan Jameel Asghar, Raghav Bhakar +2
Lack of trust between organisations and privacy concerns about their data are impediments to an otherwise potentially symbiotic joint data analysis. We propose DataRing, a data sha…
On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models
Benjamin Zi Hao Zhao, Aviral Agrawal, Catisha Coburn +5
With an increase in low-cost machine learning APIs, advanced machine learning models may be trained on private datasets and monetized by providing them as a service. However, priva…
Exploiting Behavioral Side-Channels in Observation Resilient Cognitive Authentication Schemes
Benjamin Zi Hao Zhao, Hassan Jameel Asghar, Mohamed Ali Kaafar +2
Observation Resilient Authentication Schemes (ORAS) are a class of shared secret challenge-response identification schemes where a user mentally computes the response via a cogniti…
On the Resilience of Biometric Authentication Systems against Random Inputs
Benjamin Zi Hao Zhao, Hassan Jameel Asghar, Mohamed Ali Kaafar
We assess the security of machine learning based biometric authentication systems against an attacker who submits uniform random inputs, either as feature vectors or raw inputs, in…
On Inferring Training Data Attributes in Machine Learning Models
Benjamin Zi Hao Zhao, Hassan Jameel Asghar, Raghav Bhaskar +1
A number of recent works have demonstrated that API access to machine learning models leaks information about the dataset records used to train the models. Further, the work of \ci…