InShopnito: an advanced yet privacy-friendly mobile shopping application

Mobile Shopping Applications (MSAs) are rapidly gaining popularity. They enhance the shopping experience, by offering customized recommendations or incorporating customer loyalty programs. Although MSAs are quite effective at attracting new customers and binding existing ones to a retailer's services, existing MSAs have several shortcomings. The data collection practices involved in MSAs and the lack of transparency thereof are important concerns for many customers. This paper presents inShopnito, a privacy-preserving mobile shopping application. All transactions made in inShopnito are unlinkable and anonymous. However, the system still offers the expected features from a modern MSA. Customers can take part in loyalty programs and earn or spend loyalty points and electronic vouchers. Furthermore, the MSA can suggest personalized recommendations even though the retailer cannot construct rich customer profiles. These profiles are managed on the smartphone and can be partially disclosed in order to get better, customized recommendations. Finally, we present an implementation called inShopnito, of which the security and performance is analyzed. In doing so, we show that it is possible to have a privacy-preserving MSA without having to sacrifice practicality

Fast Privacy-Preserving Punch Cards

Loyalty programs in the form of punch cards that can be redeemed for benefits have long been a ubiquitous element of the consumer landscape. However, their increasingly popular digital equivalents, while providing more convenience and better bookkeeping, pose a considerable privacy risk. This paper introduces a privacy-preserving punch card protocol that allows firms to digitize their loyalty programs without forcing customers to submit to corporate surveillance. We also present a number of extensions that allow our scheme to provide other privacy-preserving customer loyalty features. Compared to the best prior work, we achieve a $14\times$ reduction in the computation and a $11\times$ reduction in the communication required to perform a "hole punch," a $55\times$ reduction in the communication required to redeem a punch card, and a $128\times$ reduction in the computation time required to redeem a card. Much of our performance improvement can be attributed to removing the reliance on pairings or range proofs present in prior work, which has only addressed this problem in the context of more general loyalty systems. By tailoring our scheme to punch cards and related loyalty systems, we demonstrate that we can reduce communication and computation costs by orders of magnitude

A hybrid strategy for privacy-preserving recommendations for mobile shopping

To calculate recommendations, recommender systems col-lect and store huge amounts of users ’ personal data such as preferences, interaction behavior, or demographic infor-mation. If these data are used for other purposes or get into the wrong hands, the privacy of the users can be com-promised. Thus, service providers are confronted with the challenge of o↵ering accurate recommendations without the risk of dissemination of sensitive information. This paper presents a hybrid strategy combining collaborative filtering and content-based techniques for mobile shopping with the primary aim of preserving the customer’s privacy. Detailed information about the customer, such as the shopping his-tory, is securely stored on the customer’s smartphone and locally processed by a content-based recommender. Data of individual shopping sessions, which are sent to the store backend for product association and comparison with simi-lar customers, are unlinkable and anonymous. No uniquely identifying information of the customer is revealed, making it impossible to associate successive shopping sessions at the store backend. Optionally, the customer can disclose demo-graphic data and a rudimentary explicit profile for further personalization

Refocusing Loyalty Programs in the Era of Big Data: A Societal Lens Paradigm

Big data and technological change have enabled loyalty programs to become more prevalent and complex. How these developments influence society has been overlooked, both in academic research and in practice. We argue why this issue is important and propose a framework to refocus loyalty programs in the era of big data through a societal lens. We focus on three aspects of the societal lens-inequality, privacy, and sustainability. We discuss how loyalty programs in the big data era impact each of these societal factors, and then illustrate how, by adopting this societal lens paradigm, researchers and practitioners can generate insights and ideas that address the challenges and opportunities that arise from the interaction between loyalty programs and society. Our goal is to broaden the perspectives of researchers and managers so they can enhance loyalty programs to address evolving societal needs

Privacy Threats in E-Shopping (Position Paper)

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-29883-2_14E-shopping has grown considerably in the last years, providing customers with convenience, merchants with increased sales, and financial entities with an additional source of income. However, it may also be the source of serious threats to privacy. In this paper, we review the e-shopping process, discussing attacks or threats that have been analyzed in the literature for each of its stages. By showing that there exist threats to privacy in each of them, we argue our following position: “It is not enough to protect a single independent stage, as is usually done in privacy respectful proposals in this context. Rather, a complete solution is necessary spanning the overall process, dealing also with the required interconnections between stages.” Our overview also reflects the diverse types of information that e-shopping manages, and the benefits (e.g., such as loyalty programs and fraud prevention) that system providers extract from them. This also endorses the need for solutions that, while privacy preserving, do not limit or remove these benefits, if we want prevent all the participating entities from rejecting it.This work was supported by project S2013/ICE-3095-CM (CIBERDINE) of the Comunidad de Madrid and MINECO TIN2010-19607, TIN2012-30883, TIN2014-54580-R. The work of Seung Geol Choi was supported in part by the Office of Naval Research under Grant Number N0001415WX01232. The work of Moti Yung was done in part while visiting the Simons Institute for Theory of Computing, UC Berkeley. The work of Jesus Diaz was done in part while visiting the Network Security Lab at Columbia University

Black-Box Wallets: Fast Anonymous Two-Way Payments for Constrained Devices

Black-box accumulation (BBA) is a building block which enables a privacy-preserving implementation of point collection and redemption, a functionality required in a variety of user-centric applications including loyalty programs, incentive systems, and mobile payments. By definition, BBA+ schemes (Hartung et al. CCS \u2717) offer strong privacy and security guarantees, such as unlinkability of transactions and correctness of the balance flows of all (even malicious) users. Unfortunately, the instantiation of BBA+ presented at CCS \u2717 is, on modern smartphones, just fast enough for comfortable use. It is too slow for wearables, let alone smart-cards. Moreover, it lacks a crucial property: For the sake of efficiency, the user\u27s balance is presented in the clear when points are deducted. This may allow to track owners by just observing revealed balances, even though privacy is otherwise guaranteed. The authors intentionally forgo the use of costly range proofs, which would remedy this problem. We present an instantiation of BBA+ with some extensions following a different technical approach which significantly improves efficiency. To this end, we get rid of pairing groups, rely on different zero-knowledge and fast range proofs, along with a slightly modified version of Baldimtsi-Lysyanskaya blind signatures (CCS \u2713). Our prototype implementation with range proofs (for 16-bit balances) outperforms BBA+ without range proofs by a factor of 2.5. Moreover, we give estimates showing that smart-card implementations are within reach