Andreea B. Alexandru
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Researcher
My current research focus is in various flavours of fully homomorphic encryption and its threshold variants. I am particularly interested in efficiently switching between different FHE schemes and cryptographic tools in order to combine the advantages of each scheme.
During my postdoc, my research interests were in secure multi-party computation and distributed cryptographic protocols such as broadcast and consensus. Although this line of work has already spanned decades, it has gained a tremendous interest with the emergence of blockchains and, more generally, with the scaling up of distributed systems. I focus on designing efficient protocols for state machine replication and broadcast under various threat models and network synchronicity assumptions. For instance, arbitrary changes in the network synchronicity can render a protocol designed for synchronous networks insecure, while a protocol designed for asynchronous networks tolerates fewer faults even when the network is synchronous. I proposed common subset and state machine replication protocols that have low communication complexity and tolerate the optimal number of faults even under arbitrary network transitions from synchronous to asynchronous.
Other current research interests revolved around robust differential privacy, anonymous communication systems, and designing both private and secure control systems.
During PhD, my main focus was in the privacy and security of dynamical systems. Many of the structures we see around us are dynamical systems; from the time series coming from medical monitoring sensors to the energy consumption of our homes. Moreover, abstract concepts can be modeled as dynamical systems: iterative optimization algorithms like the gradient descent algorithm, as well as the processes of training and evaluating neural networks (ideas from control theory can help making decisions and controlling variables of interest in all these areas). Nevertheless, concealing dynamical data brings extra challenges, such as dealing with the dependencies between data at different time steps, maintaining privacy at consecutive iterations and accumulation of noise in the result.
My past research involved designing privacy-preserving control and optimization algorithms that make use of homomorphic encryption schemes and secure multi-party computation schemes. The idea behind these concepts is to perform computations directly on encrypted data, such that leakage of private information to the computing entity is minimized. A rough partition of my PhD research projects consists of:
- private cloud-based quadratic optimization from distributed private data;
- linear and nonlinear cloud-based control on encrypted data;
- private data-driven cloud-based control;
- oblivious distributed weighted sum aggregation.
I am interested in fully homomorphic encryption and in expanding and efficientizing its capabilities. In the summer of 2019, I worked at Duality Technologies, a start-up founded by academics in the field of cryptography, that centers on developing software for real-world encrypted computing applications. I focused on optimizing various encrypted capabilities in a proprietary version of PALISADE, a library for fully homomorphic encryption.
Another project related to privacy I worked on exploited the system's model to achieve motion planning with secrecy guarantees.
My vision for the future involves research in five areas:
- using cryptography to attain secure and reliable distributed algorithms at no performance cost;
- ensuring accountability and verifiability, under privacy requirements and abuse prevention, for networks of autonomous agents;
- bridging secure multi-party computation tools with differential privacy;
- continuing to enable trustworthy control and machine learning;
- developing lightweight privacy solutions for low-power distributed devices, vital to IoT;
- contributing to a more practical fully homomorphic encryption implementation and deployment.
[Google Scholar]
- Alexandru A. B., Blum E., Katz J. and Loss J., State Machine Replication under Changing Network Conditions, in International Conference on the Theory and Application of Cryptology and Information Security (ASIACRYPT 2022), pp. 681-710, 2022, Cham: Springer Nature Switzerland. eprint
- Alexandru A. B., Burbano L., Celiktug M. F., Gomez J., Cardenas A. A., Kantarcioglu M.,Katz J., Private Anomaly Detection in Linear Controllers: Garbled Circuits vs. Homomorphic Encryption, in Proceedings of the 61th Conference on Decision and Control (CDC), pp. 7746-7753, 2022, IEEE. paper
- Alexandru A. B., Tsiamis A. and Pappas G. J., Encrypted Distributed Lasso for Sparse Data Predictive Control, in Proceedings of the 60th Conference on Decision and Control (CDC), pp. 4901-4906, 2021, IEEE. paper, arXiv
- Alexandru A. B., Tsiamis A. and Pappas G. J., Towards Private Data-driven Control, in Proceedings of the 59th Conference on Decision and Control (CDC), pp. 5449-5456, 2020, IEEE. paper
- Alexandru A. B. and Pappas G. J., Private Weighted Sum Aggregation for Distributed Control Systems, in Proceedings of the 21st International Federation of Automatic Control (IFAC) World Congress, 2020. paper
- Alexandru A. B., Schulze Darup M. and Pappas G. J., Encrypted cooperative control revisited, in Proceedings of the 58th Conference on Decision and Control (CDC), pp. 7196-7202, 2019, IEEE. paper
- Tsiamis A., Alexandru A. B. and Pappas G. J., Motion Planning with Secrecy, in Proceedings of the American Control Conference (ACC), pp. 784-791, 2019, IEEE. Finalist for best student paper award. paper
- Alexandru A. B. and Pappas G. J., Encrypted LQG using Labeled Homomorphic Encryption, in Proceedings of 10th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), pp. 129-140, 2019. Finalist for best paper award. paper, GitHub
- Alexandru A. B., Morari M. and Pappas G. J., Cloud-based MPC with Encrypted Data, in Proceedings of the 57th Conference on Decision and Control (CDC), pp. 5014-5019 2018, IEEE. paper, arXiv extended version, GitHub
- Alexandru A.B., Pequito S., Jadbabaie A. and Pappas G.J., 2017, On the Limited Communication Analysis and Design
for Decentralized Estimation in Proceedings of the 56th Conference on Decision and Control (CDC), pp. 1713-1718, IEEE. paper, arXiv extended version
- Alexandru A.B., Gatsis K. and Pappas G.J., 2017, Privacy preserving Cloud-based Quadratic Optimization in Proceedings of the 55th Annual Allerton Conference on Communication, Control, and Computing, pp. 1168-1175, IEEE. paper
- Alexandru A.B., Pequito S., Jadbabaie A. and Pappas G.J., 2016, Decentralized observability with limited communication between sensors in Proceedings of the 55th Conference on Decision and Control (CDC), pp. 885-890, IEEE. paper, arXiv extended version
- Alexandru A.B., Lup, S., Dita B., 2013, GDS2M: Preprocessing Tool for MEMS Devices in Proceedings of the 8th International Symposium on Advanced Topics in Electrical Engineering (ATEE), pp. 1-4, IEEE. Third place in best student paper competition. paper
- Geva R., Gusev A., Polyakov Y., Liram L., Rosolio O., Alexandru A. B., Genise N., Blatt M., Duchin Z., Waissengrin B., Mirelman D., Bukstein F., Blumenthal D. T., Wolf I., Pelles-Avraham S., Schaffer T., Lavi L. A., Micciancio D., Vaikuntanathan V., Al Badawi A. and Goldwasser S., Collaborative privacy-preserving analysis of oncological data using multiparty homomorphic encryption, The Proceedings of the National Academy of Sciences (PNAS), 120(33), p.e2304415120, 2023. eprint
- Alexandru A. B., and Pappas G. J., Private Weighted Sum Aggregation, IEEE Transactions on Control of Networked Systems, 9(1), pp. 219-230, 2021. arXiv
- Schulze Darup M., Alexandru A. B., Quevedo D. E. and Pappas G. J., Encrypted control for networked systems -- An illustrative introduction and current challenges, IEEE Control Systems, 41(3), pp. 58-78, 2021. arXiv
- Alexandru A. B. and Pappas G. J., Secure Multi-party Computation for Cloud-Based Control, in "Privacy in Dynamical Systems", pp. 179-207, 2020, Springer, Singapore. arXiv.
- Alexandru A. B., Gatsis K., Shoukry Y., Seshia S. A., Tabuada P. and Pappas, G. J., Cloud-based Quadratic Optimization with Partially Homomorphic Encryption, IEEE Transactions on Automatic Control, 66(5), pp. 2357-2364, 2020. arXiv, GitHub.
- Alexandru A. B., Al Badawi A., Micciancio D. and Polyakov Y., Application-Aware Approximate Homomorphic Encryption: Configuring FHE for Practical Use, 2024. eprint
- Alexandru A. B., Loss J., Papamanthou C. and Tsimos G., Sublinear-round Broadcast without trusted setup
against dishonest majority, 2022. eprint
- Alexandru A. B., Tsiamis A. and Pappas G. J., Data-driven Control on Encrypted Data, 2020. arXiv
Posters
- Privacy for Cyber-Physical Systems, Oct. 2019, EECS Rising Stars at UIUC.
- Private Cooperative Control, Oct. 2019, Grace Hopper Celebration.
- Privacy for Cyber-Physical Systems, Mar. 2019, ECEDHA Annual Conference, iREDEFINE Workshop.
- Privacy preserving Cloud-based Quadratic Optimization, Mar. 2018, 5th Annual Women in Cybersecurity Conference.
- Secure Cloud-outsourced Optimization Problems through Homomorphic Encryption, Aug. 2017, Intel-NSF Center on Cyber Physical System Security.