6 citations · 11 across the 5 of their papers we have counts for
5 papers
Local Differentially Private Fuzzy Counting in Stream Data using Probabilistic Data Structure
Dinusha Vatsalan, Raghav Bhaskar, Mohamed Ali Kaafar
Privacy-preserving estimation of counts of items in streaming data finds applications in several real-world scenarios including word auto-correction and traffic management applicat…
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…
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…
Efficient and Dynamic Group Key Agreement in Ad hoc Networks
Raghav Bhaskar, Paul Mühlethaler, Daniel Augot +3
Confidentiality, integrity and authentication are more relevant issues in Ad hoc networks than in wired fixed networks. One way to address these issues is the use of symmetric key…