Professor Martina Cardone awarded 2022 McKnight Land-Grant Professorship
Professor Martina Cardone is a recipient of the 2022 McKnight Land-Grant Professorship for her research on privacy and security in modern communication and computing systems. The McKnight Land-Grant Professorship Program supports and advances the careers of assistant professors at a crucial point in their professional lives. Recipients hold the designation of “McKnight Land-Grant Professor” for two years.
Cardone’s research lies at the intersection of information theory, wireless networks, and algorithms, each of which enables conveniences and activities that pervade almost all aspects of modern life. Technologies and advancements such as the internet, computers, wireless communications, Google searches, and Amazon purchase recommendations are all outcomes of these three key areas. Currently, Cardone has focused her efforts on developing solutions for two critical aspects that describe the era of Big Data that we live in: security and privacy.
Data generation and transmission characterize areas as diverse as education, health, transportation, finance, education, and more. In all these areas, protocols to maintain data security and privacy while ensuring real-time communication and processing are paramount. Addressing these challenges, Cardone has led her research group to develop technologies for secure data transmission over wireless networks, and for sensitive data privatization in computing systems.
Current security mechanisms for data transmission rely on cryptographic techniques such as RSA encryption (a technique widely used by financial institutions to protect data). However such techniques are proving to be increasingly vulnerable to attacks. Another key challenge lies in the area of ensuring data privacy. Presently, there is a need to develop mechanisms that can distinguish between learning about a population versus learning about specific individuals within the population. For instance gathering information to establish a correlation between lifestyle and health can help public health experts formulate guidance. However, when a specific individual’s identity is revealed in such an instance, that is a breach of privacy and can have a detrimental impact on them.
Cardone proposes solutions that address both challenges: securely transfer, and privately process massive data volumes over communication networks and computing systems. Even as she leads her research group to design optimal solutions to these problems, she is firmly focused on ensuring that these solutions are also simple.
Secure communication in mmWave networks
Cardone’s project on secure communication in mmWave networks addresses the first challenge: secure transmission of data. With the volume of wireless data transfer increasing, the communication industry is turning toward the millimeter-wave (mmWave) spectrum comprising frequencies from 30 gigahertz to 300 gigahertz to support the high data rates. The open and shared nature of the wireless medium makes it vulnerable to eavesdropping attacks, which pose a serious threat to sensitive and confidential information in areas such as mobile banking, credit card transactions, and healthcare. The assumption that eavesdroppers have limited computational capabilities is misplaced in the light of recent advances in (quantum) computing. Yet, in large and dense mmWave networks, it is reasonable to assume that eavesdroppers can intercept transmissions only over a subset of the communication links over which they need to be physically present.
Cardone’s research in this area addresses the challenges posed by such threats by exploring and establishing protocols that can ensure secure communication over mmWave networks, in the presence of an eavesdropper with unlimited computational capabilities but limited network presence. The highlight of the project is the establishment of novel theoretical foundations that identify and model unique features of secure communication in the mmWave spectrum, and the study of their effect on the operations in the network layer. Cardone’s research is multidisciplinary, and it leverages concepts and tools from other fields including information theory, optimization and linear programming, algorithms, and graph theory. The key specifics of her project include: introduction of a theoretical framework that captures the essentials of mmWave communication and eavesdropper capabilities; characterization of the maximum secure information flow and trade-offs over mmWave networks; and finally, the development of scalable and secure communication architectures. Her research in this area is supported by a National Science Foundation (NSF) Early CAREER Award.
Ranking recovery under privacy considerations
In the area of privacy protection, Cardone is working on ranking recovery under privacy considerations. Ranking algorithms are commonly used across various computing systems. Google search, Amazon’s recommender system, and GeneRank (for molecular biology analysis) are some of the more high profile and readily recognized examples of such algorithms at work. A ranking algorithm sorts a data set in a specific order, and the resulting ranked data set can then be used for decision making purposes. For instance, Amazon’s recommender system uses a customer’s previous purchases to recommend items they might be interested in for future purchases. However, this brings into play a couple of different situations: the data set might contain confidential information, and/or the customer might not want to disclose their previous purchases. In each case, it is critical that user data is privatized. Cardone addresses this challenge by seeking mechanisms that can guarantee privacy while ensuring that the ranking algorithm offers accurate information. Her research group is seeking a solution based on the observation that for modern computing systems it is often sufficient to know the relative ranking of data points, and not necessary to know the values of the data itself. Currently, the group has developed several low-complexity algorithms that have successfully balanced privacy with utility. The research is supported by an NSF CRII (Computer and Information Science and Engineering Research Initiation Initiative) Award.
Cardone’s research is relevant on a global scale to industries and individuals, and the outcomes of her work have a wide range of applications where there is a need for protocols that provide confidentiality. Data defines and characterizes modern life and computing systems in many critical sectors: finance, healthcare and biological sciences, and retail are some of the obvious ones. Her research has been published in high-ranking journals in her area including IEEE Journal on Selected Areas in Communications, IEEE Journal on Selected Areas in Information Theory, and IEEE Transactions on Communications.
As a teacher, Cardone is a dedicated mentor to her graduate and undergraduate students. She has served as a faculty advisor to undergraduate seniors helping them navigate courses, technical area choices, career paths, and graduate school decisions. Outside the classroom, Cardone is active in increasing the participation of underrepresented populations in STEM. She is also active within the department in activities that foster diversity and inclusivity, leading discussions, delivering talks, and participating in mentoring events. Cardone’s recognition and support in the form of the McKnight Land-Grant Professorship is timely and fitting. The Department of Electrical and Computer Engineering is proud of her.
Professor Martina Cardone earned her doctoral degree in Electronics and Communications in 2015 from Télécom ParisTech (with work done at Eurecom in Sophia Antipolis, France) under the guidance of professors Raymond Knopp and Daniela Tuninetti. Her dissertation received the outstanding PhD award at Télécom ParisTech and her research on wireless cellular systems with relays was supported by the Qualcomm Innovation Fellowship. Later, she was a postdoctoral researcher at UCLA where she conducted research with professor Christina Fragouli. She joined the University of Minnesota Twin Cities in 2018. She is the recipient of the NSF CRII award in 2019, and the NSF CAREER award in 2021.