Privacy-Preserving Billing for e-Ticketing Systems in Public Transportation

Many electronic ticketing systems for public transportation have been deployed around the world. Using the example of Singapore\u27s EZ-Link system we show that it is easy to invade a traveller\u27s privacy and obtain his travel records in a real-world system. Then we propose encrypted bill processing of the travel records preventing any kind of privacy breach. Clear advantages of using bill processing instead of electronic cash are the possibility of privacy-preserving data mining analyses by the transportation company and monthly billing entailing a tighter customer relation and advanced tariffs. Moreover, we provide an implementation to demonstrate the feasibility of our solution

Privacy-Preserving Observation in Public Spaces

One method of privacy-preserving accounting or billing in cyber-physical systems, such as electronic toll collection or public transportation ticketing, is to have the user present an encrypted record of transactions and perform the accounting or billing computation securely on them. Honesty of the user is ensured by spot checking the record for some selected surveyed transactions. But how much privacy does that give the user, i.e. how many transactions need to be surveyed? It turns out that due to collusion in mass surveillance all transactions need to be observed, i.e. this method of spot checking provides no privacy at all. In this paper we present a cryptographic solution to the spot checking problem in cyber-physical systems. Users carry an authentication device that authenticates only based on fair random coins. The probability can be set high enough to allow for spot checking, but in all other cases privacy is perfectly preserved. We analyze our protocol for computational efficiency and show that it can be efficiently implemented even on plat- forms with limited computing resources, such as smart cards and smart phones

Privacy-Preserving Electronic Ticket Scheme with Attribute-based Credentials

Electronic tickets (e-tickets) are electronic versions of paper tickets, which enable users to access intended services and improve services' efficiency. However, privacy may be a concern of e-ticket users. In this paper, a privacy-preserving electronic ticket scheme with attribute-based credentials is proposed to protect users' privacy and facilitate ticketing based on a user's attributes. Our proposed scheme makes the following contributions: (1) users can buy different tickets from ticket sellers without releasing their exact attributes; (2) two tickets of the same user cannot be linked; (3) a ticket cannot be transferred to another user; (4) a ticket cannot be double spent; (5) the security of the proposed scheme is formally proven and reduced to well known (q-strong Diffie-Hellman) complexity assumption; (6) the scheme has been implemented and its performance empirically evaluated. To the best of our knowledge, our privacy-preserving attribute-based e-ticket scheme is the first one providing these five features. Application areas of our scheme include event or transport tickets where users must convince ticket sellers that their attributes (e.g. age, profession, location) satisfy the ticket price policies to buy discounted tickets. More generally, our scheme can be used in any system where access to services is only dependent on a user's attributes (or entitlements) but not their identities.Comment: 18pages, 6 figures, 2 table

Privacy-Preserving Billing for e-Ticketing Systems in Public Transportation

Abstract. Many electronic ticketing systems for public transportation have been deployed around the world. Using the example of Singapore’s EZ-Link system we show that it is easy to invade a traveller’s privacy and obtain his travel records in a real-world system. Then we propose encrypted bill processing of the travel records preventing any kind of privacy breach. Clear advantages of using bill processing instead of electronic cash are the possibility of privacy-preserving data mining analyses by the transportation company and monthly billing entailing a tighter customer relation and advanced tariffs. Moreover, we provide an implementation to demonstrate the feasibility of our solution.

Privacy-preserving E-ticketing Systems for Public Transport Based on RFID/NFC Technologies

Pervasive digitization of human environment has dramatically changed our everyday lives. New technologies which have become an integral part of our daily routine have deeply affected our perception of the surrounding world and have opened qualitatively new opportunities. In an urban environment, the influence of such changes is especially tangible and acute. For example, ubiquitous computing (also commonly referred to as UbiComp) is a pure vision no more and has transformed the digital world dramatically. Pervasive use of smartphones, integration of processing power into various artefacts as well as the overall miniaturization of computing devices can already be witnessed on a daily basis even by laypersons. In particular, transport being an integral part of any urban ecosystem have been affected by these changes. Consequently, public transport systems have undergone transformation as well and are currently dynamically evolving. In many cities around the world, the concept of the so-called electronic ticketing (e-ticketing) is being extensively used for issuing travel permissions which may eventually result in conventional paper-based tickets being completely phased out already in the nearest future. Opal Card in Sydney, Oyster Card in London, Touch & Travel in Germany and many more are all the examples of how well the e-ticketing has been accepted both by customers and public transport companies. Despite numerous benefits provided by such e-ticketing systems for public transport, serious privacy concern arise. The main reason lies in the fact that using these systems may imply the dramatic multiplication of digital traces left by individuals, also beyond the transport scope. Unfortunately, there has been little effort so far to explicitly tackle this issue. There is still not enough motivation and public pressure imposed on industry to invest into privacy. In academia, the majority of solutions targeted at this problem quite often limit the real-world pertinence of the resultant privacy-preserving concepts due to the fact that inherent advantages of e-ticketing systems for public transport cannot be fully leveraged. This thesis is aimed at solving the aforementioned problem by providing a privacy-preserving framework which can be used for developing e-ticketing systems for public transport with privacy protection integrated from the outset. At the same time, the advantages of e-ticketing such as fine-grained billing, flexible pricing schemes, and transparent use (which are often the main drivers for public to roll out such systems) can be retained

Secure and Efficient Comparisons between Untrusted Parties

A vast number of online services is based on users contributing their personal information. Examples are manifold, including social networks, electronic commerce, sharing websites, lodging platforms, and genealogy. In all cases user privacy depends on a collective trust upon all involved intermediaries, like service providers, operators, administrators or even help desk staff. A single adversarial party in the whole chain of trust voids user privacy. Even more, the number of intermediaries is ever growing. Thus, user privacy must be preserved at every time and stage, independent of the intrinsic goals any involved party. Furthermore, next to these new services, traditional offline analytic systems are replaced by online services run in large data centers. Centralized processing of electronic medical records, genomic data or other health-related information is anticipated due to advances in medical research, better analytic results based on large amounts of medical information and lowered costs. In these scenarios privacy is of utmost concern due to the large amount of personal information contained within the centralized data. We focus on the challenge of privacy-preserving processing on genomic data, specifically comparing genomic sequences. The problem that arises is how to efficiently compare private sequences of two parties while preserving confidentiality of the compared data. It follows that the privacy of the data owner must be preserved, which means that as little information as possible must be leaked to any party participating in the comparison. Leakage can happen at several points during a comparison. The secured inputs for the comparing party might leak some information about the original input, or the output might leak information about the inputs. In the latter case, results of several comparisons can be combined to infer information about the confidential input of the party under observation. Genomic sequences serve as a use-case, but the proposed solutions are more general and can be applied to the generic field of privacy-preserving comparison of sequences. The solution should be efficient such that performing a comparison yields runtimes linear in the length of the input sequences and thus producing acceptable costs for a typical use-case. To tackle the problem of efficient, privacy-preserving sequence comparisons, we propose a framework consisting of three main parts. a) The basic protocol presents an efficient sequence comparison algorithm, which transforms a sequence into a set representation, allowing to approximate distance measures over input sequences using distance measures over sets. The sets are then represented by an efficient data structure - the Bloom filter -, which allows evaluation of certain set operations without storing the actual elements of the possibly large set. This representation yields low distortion for comparing similar sequences. Operations upon the set representation are carried out using efficient, partially homomorphic cryptographic systems for data confidentiality of the inputs. The output can be adjusted to either return the actual approximated distance or the result of an in-range check of the approximated distance. b) Building upon this efficient basic protocol we introduce the first mechanism to reduce the success of inference attacks by detecting and rejecting similar queries in a privacy-preserving way. This is achieved by generating generalized commitments for inputs. This generalization is done by treating inputs as messages received from a noise channel, upon which error-correction from coding theory is applied. This way similar inputs are defined as inputs having a hamming distance of their generalized inputs below a certain predefined threshold. We present a protocol to perform a zero-knowledge proof to assess if the generalized input is indeed a generalization of the actual input. Furthermore, we generalize a very efficient inference attack on privacy-preserving sequence comparison protocols and use it to evaluate our inference-control mechanism. c) The third part of the framework lightens the computational load of the client taking part in the comparison protocol by presenting a compression mechanism for partially homomorphic cryptographic schemes. It reduces the transmission and storage overhead induced by the semantically secure homomorphic encryption schemes, as well as encryption latency. The compression is achieved by constructing an asymmetric stream cipher such that the generated ciphertext can be converted into a ciphertext of an associated homomorphic encryption scheme without revealing any information about the plaintext. This is the first compression scheme available for partially homomorphic encryption schemes. Compression of ciphertexts of fully homomorphic encryption schemes are several orders of magnitude slower at the conversion from the transmission ciphertext to the homomorphically encrypted ciphertext. Indeed our compression scheme achieves optimal conversion performance. It further allows to generate keystreams offline and thus supports offloading to trusted devices. This way transmission-, storage- and power-efficiency is improved. We give security proofs for all relevant parts of the proposed protocols and algorithms to evaluate their security. A performance evaluation of the core components demonstrates the practicability of our proposed solutions including a theoretical analysis and practical experiments to show the accuracy as well as efficiency of approximations and probabilistic algorithms. Several variations and configurations to detect similar inputs are studied during an in-depth discussion of the inference-control mechanism. A human mitochondrial genome database is used for the practical evaluation to compare genomic sequences and detect similar inputs as described by the use-case. In summary we show that it is indeed possible to construct an efficient and privacy-preserving (genomic) sequences comparison, while being able to control the amount of information that leaves the comparison. To the best of our knowledge we also contribute to the field by proposing the first efficient privacy-preserving inference detection and control mechanism, as well as the first ciphertext compression system for partially homomorphic cryptographic systems