- Program Chairs:
- Chilukuri Mohan,
- Andrew Sung
It is our great pleasure to welcome you to the 2021 ACM International Workshop on Security and Privacy Analytics (IWSPA 2021). This year's workshop is the seventh in the series and is co-hosted with the Eleventh ACM Annual Conference on Data and Application Security and Privacy (CODASPY 2021).
IWSPA addresses important research topics associated with the application of data analytics tools (including statistical, machine learning, data mining, and natural language processing) to challenges that arise with security and privacy preservation. IWSPA provides a forum for the interaction between researchers in these areas, identifying and pursuing new topics that arise in the intersection between the fields of Artificial Intelligence and Cybersecurity.
The IWSPA 2021 call for papers attracted 22 papers from four continents (Africa, Asia, Europe and North America). Each paper considered for presentation was evaluated by three reviewers, who were either committee members or assigned by committee members. The reviews were detailed and examined various aspects of the papers, including correctness and presentation. Five papers were accepted for presentation as full papers (11-page limit) and three were accepted as short papers (7-page limit).
We thank the authors, reviewers, and program committee members, whose enthusiastic efforts make the workshop possible, and are critical in its success. We also thank the CODASPY publicity chairs, workshop chair and general chair, as well as the ACM Special Interest Group on Security, Audit and Control (SIGSAC), for supporting IWSPA '21. Special thanks are also due to Rakesh Verma, former IWSPA chair, for his guidance during the entire process of organizing IWSPA '21.
Proceeding Downloads
AI vs. AI: Exploring the Intersections of AI and Cybersecurity
The future of cybersecurity will pit AI against AI. In this talk, we explore the role of AI in strengthening security defenses as well as the role of security in protecting AI services. We expect that the scale, scope and frequency of cyber attacks will ...
Large Feature Mining and Deep Learning in Multimedia Forensics
As one of the most interesting areas in cyber forensics, multimedia forensics faces many challenges as users are generating a humongous amount of data with different operations. Forgery detection and steganography detection are two hotspots in ...
WeStat: a Privacy-Preserving Mobile Data Usage Statistics System
The preponderance of smart devices, such as smartphones, has boosted the development and use of mobile applications (apps) in the recent years. This prevalence induces a large volume of mobile app usage data. The analysis of such information could lead ...
EMPAware: Analyzing Changes in User Perceptions of Mobile Privacy on iOS with Enhanced Awareness
Smartphones contain intimate details of users that are inferred from collected data or explicitly stored on the device. These details include daily travel patterns including most frequently visited locations, private photos, addresses and birthdays of ...
PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting
When multiple parties that deal with private data aim for a collaborative prediction task such as medical image classification, they are often constrained by data protection regulations and lack of trust among collaborating parties. If done in a privacy-...
SDGchain: When Service Dependency Graph Meets Blockchain to Enhance Privacy
Nowadays, the number of services is increasing which allows users to perform their tasks easily. A huge amount of data is published regularly where the personal data takes the lion's share. In fact, personal data protection remains a big issue as far as ...
A Scalable Role Mining Approach for Large Organizations
Role-based access control (RBAC) model has gained significant attention in cybersecurity in recent years. RBAC restricts system access only to authorized users based on the roles and regulations within an organization. The flexibility and usability of ...
TrollHunter2020: Real-time Detection of Trolling Narratives on Twitter During the 2020 U.S. Elections
This paper presents TrollHunter2020, a real-time detection mechanism we used to hunt for trolling narratives on Twitter during and in the aftermath of the 2020 U.S. elections. Trolling narratives form on Twitter as alternative explanations of polarizing ...
Detecting Telephone-based Social Engineering Attacks using Scam Signatures
As social engineering attacks have become prevalent, people are increasingly convinced to give their important personal or financial information to attackers. Telephone scams are common and less well-studied than phishing emails. We have found that ...
An Empirical Evaluation of Automated Machine Learning Techniques for Malware Detection
Nowadays, it is increasingly difficult even for a machine learning expert to incorporate all of the recent best practices into their modeling due to the fast development of state-of-the-art machine learning techniques. For the applications that handle ...