16 research outputs found
Cloud-based Quadratic Optimization with Partially Homomorphic Encryption
The development of large-scale distributed control systems has led to the
outsourcing of costly computations to cloud-computing platforms, as well as to
concerns about privacy of the collected sensitive data. This paper develops a
cloud-based protocol for a quadratic optimization problem involving multiple
parties, each holding information it seeks to maintain private. The protocol is
based on the projected gradient ascent on the Lagrange dual problem and
exploits partially homomorphic encryption and secure multi-party computation
techniques. Using formal cryptographic definitions of indistinguishability, the
protocol is shown to achieve computational privacy, i.e., there is no
computationally efficient algorithm that any involved party can employ to
obtain private information beyond what can be inferred from the party's inputs
and outputs only. In order to reduce the communication complexity of the
proposed protocol, we introduced a variant that achieves this objective at the
expense of weaker privacy guarantees. We discuss in detail the computational
and communication complexity properties of both algorithms theoretically and
also through implementations. We conclude the paper with a discussion on
computational privacy and other notions of privacy such as the non-unique
retrieval of the private information from the protocol outputs
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Cloud-Based Quadratic Optimization with Partially Homomorphic Encryption
This article develops a cloud-based protocol for a constrained quadratic optimization problem involving multiple parties, each holding private data. The protocol is based on the projected gradient ascent on the Lagrange dual problem and exploits partially homomorphic encryption and secure communication techniques. Using formal cryptographic definitions of indistinguishability, the protocol is shown to achieve computational privacy. We show the implementation results of the protocol and discuss its computational and communication complexity. We conclude this article with a discussion on privacy notions
Private Computation of Polynomials over Networks
This study concentrates on preserving privacy in a network of agents where
each agent seeks to evaluate a general polynomial function over the private
values of her immediate neighbors. We provide an algorithm for the exact
evaluation of such functions while preserving privacy of the involved agents.
The solution is based on a reformulation of polynomials and adoption of two
cryptographic primitives: Paillier as a Partially Homomorphic Encryption scheme
and multiplicative-additive secret sharing. The provided algorithm is fully
distributed, lightweight in communication, robust to dropout of agents, and can
accommodate a wide class of functions. Moreover, system theoretic and secure
multi-party conditions guaranteeing the privacy preservation of an agent's
private values against a set of colluding agents are established. The
theoretical developments are complemented by numerical investigations
illustrating the accuracy of the algorithm and the resulting computational
cost.Comment: 11 pages, 2 figure
Private Computation of Polynomials over Networks
This study concentrates on preserving privacy in a network of agents where
each agent seeks to evaluate a general polynomial function over the private
values of her immediate neighbors. We provide an algorithm for the exact
evaluation of such functions while preserving privacy of the involved agents.
The solution is based on a reformulation of polynomials and adoption of two
cryptographic primitives: Paillier as a Partially Homomorphic Encryption scheme
and multiplicative-additive secret sharing. The provided algorithm is fully
distributed, lightweight in communication, robust to dropout of agents, and can
accommodate a wide class of functions. Moreover, system theoretic and secure
multi-party conditions guaranteeing the privacy preservation of an agent's
private values against a set of colluding agents are established. The
theoretical developments are complemented by numerical investigations
illustrating the accuracy of the algorithm and the resulting computational
cost.Comment: 11 pages, 2 figure
Truthful and Faithful Monetary Policy for a Stablecoin Conducted by a Decentralised, Encrypted Artificial Intelligence
The Holy Grail of a decentralised stablecoin is achieved on rigorous
mathematical frameworks, obtaining multiple advantageous proofs: stability,
convergence, truthfulness, faithfulness, and malicious-security. These
properties could only be attained by the novel and interdisciplinary
combination of previously unrelated fields: model predictive control, deep
learning, alternating direction method of multipliers (consensus-ADMM),
mechanism design, secure multi-party computation, and zero-knowledge proofs.
For the first time, this paper proves:
- the feasibility of decentralising the central bank while securely
preserving its independence in a decentralised computation setting
- the benefits for price stability of combining mechanism design, provable
security, and control theory, unlike the heuristics of previous stablecoins
- the implementation of complex monetary policies on a stablecoin, equivalent
to the ones used by central banks and beyond the current fixed rules of
cryptocurrencies that hinder their price stability
- methods to circumvent the impossibilities of Guaranteed Output Delivery
(G.O.D.) and fairness: standing on truthfulness and faithfulness, we reach
G.O.D. and fairness under the assumption of rational parties
As a corollary, a decentralised artificial intelligence is able to conduct
the monetary policy of a stablecoin, minimising human intervention