Privacy in Distributed Computations based on Real Number Secret Sharing
This work addresses privacy concerns for individuals in distributed data processing, but it is incremental as it builds on existing secret sharing methods by extending them to real numbers.
The paper tackles the problem of privacy preservation in distributed computations by proposing a real number secret sharing scheme that operates directly on real numbers, eliminating the need for integer conversion. Simulations show it is much more efficient in terms of accuracy compared to integer-based schemes, though it is not perfectly secure, with leaked information bounded and asymptotically approaching zero.
Privacy preservation in distributed computations is an important subject as digitization and new technologies enable collection and storage of vast amounts of data, including private data belonging to individuals. To this end, there is a need for a privacy preserving computation framework that minimises the leak of private information during computations while being efficient enough for practical usage. This paper presents a step towards such a framework with the proposal of a real number secret sharing scheme that works directly on real numbers without the need for conversion to integers which is the case in related schemes. The scheme offers computations like addition, multiplication, and division to be performed directly on secret shared data (the cipher text version of the data). Simulations show that the scheme is much more efficient in terms of accuracy than its counterpart version based on integers and finite field arithmetic. The drawback with the proposed scheme is that it is not perfectly secure. However, we provide a privacy analysis of the scheme, where we show that the leaked information can be upper bounded and asymptotically goes to zero. To demonstrate the scheme, we use it to perform Kalman filtering directly on secret shared data.