activity
20142021
most citedLocal Differential Privacy for Deep Learning

242 citations · 380 across the 11 of their papers we have counts for

collaborators

11 papers

cs.CR2021★ 8 cited

Snipuzz: Black-box Fuzzing of IoT Firmware via Message Snippet Inference

Xiaotao Feng, Ruoxi Sun, Xiaogang Zhu +5

The proliferation of Internet of Things (IoT) devices has made people's lives more convenient, but it has also raised many security concerns. Due to the difficulty of obtaining and…

cs.CR2020★ 6 cited

PThammer: Cross-User-Kernel-Boundary Rowhammer through Implicit Accesses

Zhi Zhang, Yueqiang Cheng, Dongxi Liu +3

Rowhammer is a hardware vulnerability in DRAM memory, where repeated access to memory can induce bit flips in neighboring memory locations. Being a hardware vulnerability, rowhamme…

cs.CR2020★ 16 cited

PPaaS: Privacy Preservation as a Service

Pathum Chamikara Mahawaga Arachchige, Peter Bertok, Ibrahim Khalil +2

Personally identifiable information (PII) can find its way into cyberspace through various channels, and many potential sources can leak such information. Data sharing (e.g. cross-…

cs.CR2020★ 17 cited

Towards Practical Encrypted Network Traffic Pattern Matching for Secure Middleboxes

Shangqi Lai, Xingliang Yuan, Shi-Feng Sun +4

Network Function Virtualisation (NFV) advances the adoption of composable software middleboxes. Accordingly, cloud data centres become major NFV vendors for enterprise traffic proc…

cs.CR2019★ 3 cited

TeleHammer: A Formal Model of Implicit Rowhammer

Zhi Zhang, Yueqiang Cheng, Dongxi Liu +2

The rowhammer bug allows an attacker to gain privilege escalation or steal private data. A key requirement of all existing rowhammer attacks is that an attacker must have access to…

cs.CR2019★ 4 cited

Enabling Efficient Privacy-Assured Outlier Detection over Encrypted Incremental Datasets

Shangqi Lai, Xingliang Yuan, Amin Sakzad +3

Outlier detection is widely used in practice to track the anomaly on incremental datasets such as network traffic and system logs. However, these datasets often involve sensitive i…

cs.LG2019★ 242 cited

Local Differential Privacy for Deep Learning

M. A. P. Chamikara, P. Bertok, I. Khalil +3

The internet of things (IoT) is transforming major industries including but not limited to healthcare, agriculture, finance, energy, and transportation. IoT platforms are continual…

cs.CR2019★ 39 cited

GraphSE: An Encrypted Graph Database for Privacy-Preserving Social Search

Shangqi Lai, Xingliang Yuan, Shi-Feng Sun +3

In this paper, we propose GraphSE, an encrypted graph database for online social network services to address massive data breaches. GraphSE preserves the functionality of s…

cs.CR2019★ 16 cited

A Blockchain-based Self-tallying Voting Scheme in Decentralized IoT

Yannan Li, Willy Susilo, Guomin Yang +3

The Internet of Things (IoT) is experiencing explosive growth and has gained extensive attention from academia and industry in recent years. Most of the existing IoT infrastructure…

cs.CR2018★ 25 cited

KASR: A Reliable and Practical Approach to Attack Surface Reduction of Commodity OS Kernels

Zhi Zhang, Yueqiang Cheng, Surya Nepal +3

Commodity OS kernels have broad attack surfaces due to the large code base and the numerous features such as device drivers. For a real-world use case (e.g., an Apache Server), man…

cs.CR2014★ 4 cited

Privacy-Preserving and Outsourced Multi-User k-Means Clustering

Bharath K. Samanthula, Fang-Yu Rao, Elisa Bertino +2

Many techniques for privacy-preserving data mining (PPDM) have been investigated over the past decade. Often, the entities involved in the data mining process are end-users or orga…