Biblio

In today’s world, digital data are enormous due to technologies that advance data collection, storage, and analyses. As more data are shared or publicly available, privacy is of great concern. Having privacy means having control over your data. The first step towards privacy protection is to understand various aspects of privacy and have the ability to quantify them. Much work in structured data, however, has focused on approaches to transforming the original data into a more anonymous form (via generalization and suppression) while preserving the data integrity. Such anonymization techniques count data instances of each set of distinct attribute values of interest to signify the required anonymity to protect an individual’s identity or confidential data. While this serves the purpose, our research takes an alternative approach to provide quick privacy measures by way of anonymity especially when dealing with large-scale data. This paper presents a study of anonymity measures based on their relevant properties that impact privacy. Specifically, we identify three properties: uniformity, variety, and diversity, and formulate their measures. The paper provides illustrated examples to evaluate their validity and discusses the use of multi-aspects of anonymity and privacy measures.

In threshold schemes one represents each sensitive variable by a number n of shares such that their (usually) bitwise sum equals that variable. These shares are initially generated in such a way that any subset of n-1 shares gives no information about the sensitive variable. Functions (S-boxes, mixing layers, round functions, etc.) are computed on the shares of the inputs resulting in the output as a number of shares. An essential property of a threshold implementation of a function is that each output share is computed from at most n-1 input shares. This is called incompleteness and guarantees that that computation cannot leak information about sensitive variables. The resulting output is then typically subject to some further computation, again in the form of separate, incomplete, computation on shares. For these subsequent computations to not leak information about the sensitive variables, the output of the previous stage must still be uniform. Hence, in an iterative cryptographic primitive such as a block cipher, we need a threshold implementation of the round function that yields a uniformly shared output if its input is uniformly shared. This property of the threshold implementation is called uniformity. Threshold schemes form a good protection mechanism against differential power analysis (DPA). In particular, using it allows building cryptographic hardware that is guaranteed to be unattackable with first-order DPA, assuming certain leakage models of the cryptographic hardware at hand and for a plausible definition of "first order". Constructing an incomplete threshold implementation of a non-linear function is rather straightforward. To offer resistance against first-order DPA, the number of shares equals the algebraic degree of the function plus one. However, constructing one that is at the same time incomplete and uniform may present a challenge. For instance, for the Keccak non-linear layer, incomplete 3-share threshold implementations are easy to generate but no uniform one is known. Exhaustive investigations have been performed on all small S-boxes (3 to 5 bits) and there are many S-boxes for which it is not known to build uniform threshold implementations with d+1 shares if their algebraic degree is d. Uniformity of a threshold implementation is essential in its information-theoretical proof of resistance against first-order DPA. However, given a non-uniform threshold implementation, it is not immediate how to exploit its non-uniformity in an attack. In my talk I discuss the local and global effects of non-uniformity in iterated functions and their significance on the resistance against DPA. I treat methods to quantitatively limit the amount of non-uniformity and to keep it away from where it may be harmful. These techniques are relatively cheap and can reduce non-uniformity to such a low level that it would require an astronomical amount of samples to measure it.