Paper 2013/818

On the Relation of Random Grid, Probabilistic and Deterministic Visual Cryptography

Roberto De Prisco and Alfredo De Santis


Visual cryptography is a special type of secret sharing. Two models of visual cryptography have been independently studied: deterministic visual cryptography, introduced by Naor and Shamir, and random grid visual cryptography, introduced by Kafri and Keren. In the context of the deterministic model, Yang has introduced a third model, the probabilistic visual cryptography model. The connection between the probabilistic and the deterministic models have been explored. In this paper we show that there is a strict relation between the random grid model and the deterministic model. More specically we show that to any random grid scheme corresponds a deterministic scheme and viceversa. This allows us to use results known in a model also in the other model. In fact, the random grid model is equivalent to the probabilistic model with no pixel expansion. Exploiting the (many) results known in the deterministic model we are able to improve several schemes and to provide many upper bounds for the random grid model. Exploiting some results known for the random grid model, we are also able to provide new schemes for the deterministic model. A side eect of this paper is that future new results for any one of the two models (random grid and deterministic) should not ignore, and in fact be compared to, the results known in the other model.

Available format(s)
Publication info
Preprint. MINOR revision.
secret sharingvisual cryptography
Contact author(s)
robdep @ unisa it
2013-12-06: received
Short URL
Creative Commons Attribution


      author = {Roberto De Prisco and Alfredo De Santis},
      title = {On the Relation of Random Grid, Probabilistic and Deterministic Visual Cryptography},
      howpublished = {Cryptology ePrint Archive, Paper 2013/818},
      year = {2013},
      note = {\url{}},
      url = {}
Note: In order to protect the privacy of readers, does not use cookies or embedded third party content.