We describe an architecture and a trial implementation of a privacy-preserving location sharing system called Albatross. The system protects location information from the service provider and yet enables fine-grained location-sharing. One main feature of the system is to protect an individual's social network structure. The pattern of location sharing preferences towards contacts can reveal this structure without any knowledge of the locations themselves. Albatross protects locations sharing preferences through protocol unification and masking. Albatross has been implemented as a standalone solution, but the technology can also be integrated into location-based services to enhance privacy.

We present techniques to characterize which data contributes most to the accuracy of a recommendation algorithm. Our main technique is called differential data analysis. The name is inspired by other sorts of differential analysis, such as differential power analysis and differential cryptanalysis, where insight comes through analysis of slightly differing inputs. In differential data analysis we chunk the data and compare results in the presence or absence of each chunk. We apply differential data analysis to two datasets and three different attributes. The first attribute is called user hardship. This is a novel attribute, particularly relevant to location datasets, that indicates how burdensome a data point was to achieve. The second and third attributes are more standard: timestamp and user rating. For user rating, we confirm previous work concerning the increased importance to the recommender of high and low user ratings.

The privacy of location-based services has gained attention with their increased popularity. To date, citing insufficient privacy demand and inefficient/immature privacy preserving technologies, service providers have not been willing to build private-enhanced systems in which they do not have access to users' location information. However, current practice is likely to change in coming years with increasing privacy awareness and technological advances. For instance, Narayanan et al. recently introduced a fast private equality testing protocol for proximity testing with an untrusted server. In the current work, based on basic notions of geometry and linear algebra, we describe a new three-party protocol for solving the same problem. Our proposed protocol decreases the number of encryptions needed and gives a more efficient solution for private equivalence testing.

ABSTRACT Computational constraints mostly driven by the cost concerns of radio frequency identification tags only permit to have security schemes that are simple combinations of elementary operations rather than the bullet proven yet computationally intensive cryptographic primitives. As ultra‐lightweight authentication protocols use only basic bitwise and arithmetic operations such as XOR, OR, and addition modulo powers of 2, they are considered as a class of these schemes. Because most of the early proposed ultra‐lightweight authentication protocols are easily broken, it turned out that designing such protocols is a much deeper task than presumed. This study analyzes a modified version of the stable lightweight mutual authentication protocol denoted by SLMAP*. After exploiting an unnoticed flaw in its design rationale, it is shown that some of the freshly produced variables can be assigned to different values for the reader and the tag that presumably causes a desynchronization vulnerability. Copyright © 2011 John Wiley & Sons, Ltd.

Modular multiplication is the key ingredient needed to realize most public-key cryptographic primitives. In a modular setting, multiplications are carried in two steps: namely a usual integer arithmetic followed by a reduction step. Progress in any of these steps naturally improves the modular multiplication but it is not possible to interleave the best algorithms of these stages. In this study, we propose architectures for recently proposed method of interleaving the Karatsuba-Ofman multiplier and bipartite modular reduction on the upper most layer of Karatsuba-Ofman's recursion. We manage to come up with a high performance modular multiplication architecture by taking the advantage of a fast multiplication and a parallel reduction method.

We describe a new method to perform the modular exponentiation operation, i.e., the computation of c = memod n, where c, m, e and n are large integers. The new method uses the discrete Fourier transform over a finite ring, and relies on new techniques to perform multiplication and reduction operations. The method yields efficient and highly parallel architectures for hardware realizations of public-key cryptosystems requiring the modular exponentiation as the core computation, such as the RSA and Diffie-Hellman algorithms.

It is widely recognized that data security will play a central role in future IT systems. Providing public-key cryptographic primitives, which are the core tools for security, is often difficult on embedded processor due to computational, memory, and power constraints. This contribution appears to be the first thorough comparison of two public-key families, namely elliptic curve (ECC) and hyperelliptic curve cryptosystems on a wide range of embedded processor types (ARM, ColdFire, PowerPC). We investigated the influence of the processor type, resources, and architecture regarding throughput. Further, we improved previously known HECC algorithms resulting in a more efficient arithmetic.