Sharif Digital Repository

We consider the problem of private information retrieval (PIR), where a single user with private side information (PSI) aims to retrieve multiple files from a library stored at some servers. We assume that the side information (SI) at the user includes a subset of files stored privately. Moreover, the identity of requests and side information at the user are not revealed to any of the servers. The problem involves finding the minimum load transmitted from the servers to the user such that the requested files can be decoded with the help of received data and side information. By providing matching lower and upper bounds for certain regimes, we characterize the minimum load imposed on all the... 

We consider the problem of private information retrieval (PIR) where a single user with private side information aims to retrieve multiple files from a library stored (uncoded) at a number of servers. We assume the side information at the user includes a subset of files stored privately (i.e., the server does not know the indices of these files). In addition, we require that the identity of requests and side information at the user are not revealed to any of the servers. The problem involves finding the minimum load to be transmitted from the servers to the user such that the requested files can be decoded with the help of received and side information. By providing matching lower and upper... 

In this paper, we study the problem of private function computation, where a user wants to compute a function of some inputs, using N in mathbb{N} servers, where the function is a private combination/composition of some K in mathbb{N} public basic functions {f 1 , f 2 ,., f K }. More precisely, for some inputs W m , m [1 : M], the user's goal is to calculate hleft( {{W-m}} ight) = sumolimits-{j = 1}^J {{alpha -j}} {h-j}left( {{W-m}} ight), for some J in mathbb{N}, some scalers α j , j [1 : J], and some functions h j (.), j [1 : J], where each is an arbitrary compositions of the basic functions {f 1 , f 2 ,., f K }. The computation is done through a sequence of queries to N servers. In each... 

The widespread use of cloud computing services raises the question of how one can delegate the processing tasks to the untrusted distributed parties without breaching the privacy of its data and algorithms. Motivated by the algorithm privacy concerns in a distributed computing system, in this paper, we introduce the private function retrieval (PFR) problem, where a user wishes to efficiently retrieve a linear function of K messages from N non-communicating replicated servers while keeping the function hidden from each individual server. The goal is to find a scheme with minimum communication cost. To characterize the fundamental limits of the communication cost, we define the capacity of PFR... 

In this paper, we argue that in many basic algorithms for machine learning, including support vector machine (SVM) for classification, principal component analysis (PCA) for dimensionality reduction, and regression for dependency estimation, we need the inner products of the data samples, rather than the data samples themselves.Motivated by the above observation, we introduce the problem of private inner product retrieval for distributed machine learning, where we have a system including a database of some files, duplicated across some non-colluding servers. A user intends to retrieve a subset of specific size of the set of the inner product of every pair of data items in the database with...