Privacy Properties Research Articles

Towards a Unified Model of Accountability Infrastructures

Accountability aims to provide explanations for why unwanted situations occurred, thus providing means to assign responsibility and liability. As such, accountability has slightly different meanings across the sciences. In computer science, our focus is on providing explanations for technical systems, in particular if they interact with their physical environment using sensors and actuators and may do serious harm. Accountability is relevant when considering safety, security and privacy properties and we realize that all these incarnations are facets of the same core idea. Hence, in this paper we motivate and propose a model for accountability infrastructures that is expressive enough to capture all of these domains. At its core, this model leverages formal causality models from the literature in order to provide a solid reasoning framework. We show how this model can be instantiated for several real-world use cases.

Self-tallying quantum anonymous voting

Anonymous voting is a voting method of hiding the link between a vote and a voter, the context of which ranges from governmental elections to decision making in small groups like councils or companies. In this paper, we propose a quantum anonymous voting protocol assisted by two kinds of entangled quantum states. Particularly, we provide a mechanism of opening and permuting the ordered votes of all the voters in an anonymous manner; any party, who is interested in the voting results, can acquire a permutation copy, and then obtains the voting result through simple calculation. Unlike all previous quantum works on anonymous voting, our quantum anonymous protocol firstly possesses the properties of privacy, self-tallying, non-reusability, verifiability and fairness at the same time. Besides, we demonstrate that the entanglement of the novel quantum states used in our protocol makes the attack from outside eavesdropper and inside dishonest voters impossible. We also generalize our protocol to execute tasks of anonymous multi-party computation, such as anonymous broadcast and anonymous ranking.

Dynamic and Efficient Private Keyword Search over Inverted Index--Based Encrypted Data

Querying over encrypted data is gaining increasing popularity in cloud-based data hosting services. Security and efficiency are recognized as two important and yet conflicting requirements for querying over encrypted data. In this article, we propose an efficient private keyword search (EPKS) scheme that supports binary search and extend it to dynamic settings (called DEPKS ) for inverted index--based encrypted data. First, we describe our approaches of constructing a searchable symmetric encryption (SSE) scheme that supports binary search. Second, we present a novel framework for EPKS and provide its formal security definitions in terms of plaintext privacy and predicate privacy by modifying Shen et al.’s security notions [Shen et al. 2009]. Third, built on the proposed framework, we design an EPKS scheme whose complexity is logarithmic in the number of keywords. The scheme is based on the groups of prime order and enjoys strong notions of security, namely statistical plaintext privacy and statistical predicate privacy. Fourth, we extend the EPKS scheme to support dynamic keyword and document updates. The extended scheme not only maintains the properties of logarithmic-time search efficiency and plaintext privacy and predicate privacy but also has fewer rounds of communications for updates compared to existing dynamic search encryption schemes. We experimentally evaluate the proposed EPKS and DEPKS schemes and show that they are significantly more efficient in terms of both keyword search complexity and communication complexity than existing randomized SSE schemes.

Differential privacy for real smart metering data

The collection of detailed consumption data through smart metering has led to privacy concerns. Aggregating the consumption data over a number of smart meters can be used to strike a balance between functional and privacy requirements. A number of contributions have proposed the use of differential privacy in smart metering to perturb aggregates in order to provide a proven privacy property for end consumers. However, as differential privacy has originally been proposed for very large datasets, the applicability in real-world smart metering is not guaranteed. In this paper, the effect of differential privacy on real smart metering data is studied, especially with respect to balancing utility and privacy requirements. The main finding is that even after some improvements of the basic method the aggregation group size must be of the order of thousands of smart meters in order to have reasonable utility.

On compositional reasoning about anonymity and privacy in epistemic logic

In this paper, we exploit epistemic logic (or the modal logic of knowledge) for multiagent systems to discuss the compositionality of several privacy-related information-hiding/disclosure properties. The properties considered here are anonymity, privacy, onymity, and identity. Our initial observation reveals that anonymity/privacy properties are not necessarily sequentially compositional. This means that even though a system comprising several sequential phases satisfies a certain unlinkability property in each phase, the entire system does not always enjoy a desired unlinkability property. We show that the compositionality can be guaranteed provided that the phases of the system satisfy what we call independence assumptions. More specifically, we develop a series of theoretical case studies of what assumptions are sufficient to guarantee the sequential compositionality of various degrees of anonymity, privacy, onymity, and/or identity properties. Similar results for parallel composition are also discussed. Further, we use the probabilistic extension of epistemic logic to consider the compositionality of probabilistic anonymity/privacy. We show that the compositionality can also be guaranteed in the probabilistic setting, provided that the phases of the system satisfy a probabilistic independence assumption.

Evaluating the privacy properties of telephone metadata

Since 2013, a stream of disclosures has prompted reconsideration of surveillance law and policy. One of the most controversial principles, both in the United States and abroad, is that communications metadata receives substantially less protection than communications content. Several nations currently collect telephone metadata in bulk, including on their own citizens. In this paper, we attempt to shed light on the privacy properties of telephone metadata. Using a crowdsourcing methodology, we demonstrate that telephone metadata is densely interconnected, can trivially be reidentified, and can be used to draw sensitive inferences.

Black-Box Accumulation: Collecting Incentives in a Privacy-Preserving Way

Abstract We formalize and construct black-box accumulation (BBA), a useful building block for numerous important user-centric protocols including loyalty systems, refund systems, and incentive systems (as, e.g., employed in participatory sensing and vehicle-to-grid scenarios). A core requirement all these systems share is a mechanism to let users collect and sum up values (call it incentives, bonus points, reputation points, etc.) issued by some other parties in a privacy-preserving way such that curious operators may not be able to link the different transactions of a user. At the same time, a group of malicious users may not be able to cheat the system by pretending to have collected a higher amount than what was actually issued to them. As a first contribution, we fully formalize the core functionality and properties of this important building block. Furthermore, we present a generic and non-interactive construction of a BBA system based on homomorphic commitments, digital signatures, and non-interactive zero-knowledge proofs of knowledge. For our construction, we formally prove security and privacy properties. Finally, we propose a concrete instantiation of our construction using Groth-Sahai commitments and proofs as well as the optimal structure-preserving signature scheme of Abe et al. and analyze its efficiency.

PIMRS: achieving privacy and integrity-preserving multi-owner ranked-keyword search over encrypted cloud data

Because of the flexibility and convenience brought by cloud computing, it has been adopted in many applications. To preserve the privacy of cloud data, data owner often encrypts all sensitive data files, which makes the keyword search application based on plaintext a very challenging task. Therefore, several privacy-preserving keyword search algorithms have been developed recently, and most of these works support only single-data-owner settings. However, there are always more than one data owners in real applications, which are much more complex and challenging than single-owner scenario. To support multi-owner keyword search, those prior search algorithms need to be repeated several times, because each data owner intends to encrypt his own files with a unique private-key separately. It is absolutely not an efficient way. In this paper, we propose a privacy and integrity-preserving multi-owner ranked-keyword search scheme termed PIMRS. In the PIMRS, we exploit an asymmetric scalar-product encryption function based on the TF×IDF rule to preserve data privacy and to obtain more precise search results. Furthermore, a circular bi-direction-linked list based scheme is proposed to preserve the integrity of search results, which also enables the misbehaviors of cloud server to be detected. The security analysis of PIMRS shows its privacy and integrity property, and extensive experiments based on real-world data set confirm the high efficiency of proposed schemes. Copyright © 2016 John Wiley & Sons, Ltd.

Preserving differential privacy under finite-precision semantics

The approximation introduced by finite-precision representation of continuous data can induce arbitrarily large information leaks even when the computation using exact semantics is secure. Such leakage can thus undermine design efforts aimed at protecting sensitive information. We focus here on differential privacy, an approach to privacy that emerged from the area of statistical databases and is now widely applied also in other domains. In this approach, privacy is protected by adding noise to the values correlated to the private data. The typical mechanisms used to achieve differential privacy have been proved correct in the ideal case in which computations are made using infinite-precision semantics. In this paper, we analyze the situation at the implementation level, where the semantics is necessarily limited by finite precision, i.e., the representation of real numbers and the operations on them are rounded according to some level of precision. We show that in general there are violations of the differential privacy property, and we study the conditions under which we can still guarantee a limited (but, arguably, acceptable) variant of the property, under only a minor degradation of the privacy level.

Unpicking PLAID: a cryptographic analysis of an ISO-standards-track authentication protocol

The Protocol for Lightweight Authentication of Identity (PLAID) aims at secure and private authentication between a smart card and a terminal. Originally developed by a unit of the Australian Department of Human Services for physical and logical access control, PLAID has now been standardized as an Australian standard AS-5185-2010 and is currently in the fast-track standardization process for ISO/IEC 25185-1. We present a cryptographic evaluation of PLAID. As well as reporting a number of undesirable cryptographic features of the protocol, we show that the privacy properties of PLAID are significantly weaker than claimed: using a variety of techniques, we can fingerprint and then later identify cards. These techniques involve a novel application of standard statistical and data analysis techniques in cryptography. We discuss potential countermeasures to our attacks and comment on our experiences with the standardization process of PLAID.

Improvement of an Eliptic Curve Based Threshold Proxy Signature Scheme

In 2006, Pomykala et al. proposed an elliptic curve based threshold proxy signature scheme as well as a proxy-protected version. They claimed their scheme had the properties of secrecy, unforgeability, non-repudiation and signer’s identification. In this study, we showed inter conspiracy attack on the basic scheme and gave another attack on the proxy-protected version. We pointed out that even if the central authority is engaged, the basic model is not a proxy-protected one. We also proposed an improved scheme that can resist the known attacks to achieve higher security, and gave the security analysis of the improved scheme in detail.

Security, Privacy, and Incentive Provision for Mobile Crowd Sensing Systems

Recent advances in sensing, computing, and networking have paved the way for the emerging paradigm of mobile crowd sensing (MCS). The openness of such systems and the richness of data MCS users are expected to contribute to them raise significant concerns for their security, privacy-preservation and resilience. Prior works addressed different aspects of the problem. But in order to reap the benefits of this new sensing paradigm, we need a holistic solution. That is, a secure and accountable MCS system that preserves user privacy, and enables the provision of incentives to the participants. At the same time, we are after an MCS architecture that is resilient to abusive users and guarantees privacy protection even against multiple misbehaving and intelligent MCS entities (servers). In this paper, we meet these challenges and propose a comprehensive security and privacy-preserving architecture. With a full blown implementation, on real mobile devices, and experimental evaluation we demonstrate our system’s efficiency, practicality, and scalability. Last but not least, we formally assess the achieved security and privacy properties. Overall, our system offers strong security and privacy-preservation guarantees, thus, facilitating the deployment of trustworthy MCS applications.

Secure and controllable [formula omitted]-NN query over encrypted cloud data with key confidentiality

To enjoy the advantages of cloud service while preserving security and privacy, huge data are increasingly outsourced to cloud in encrypted form. Unfortunately, most conventional encryption schemes cannot smoothly support encrypted data analysis and processing. As a significant topic, several schemes have been recently proposed to securely compute k-nearest neighbors (k-NN) on encrypted data being outsourced to cloud server (CS). However, most existing k-NN search methods assume query users (QUs) are fully-trusted and know the key of data owner (DO) to encrypt/decrypt outsourced database. It is not realistic in many situations.In this paper, we propose a new secure k-NN query scheme on encrypted cloud data. Our approach simultaneously achieves: (1) data privacy against CS: the encrypted database can resist potential attacks of CS, (2) key confidentiality against QUs: to avoid the problems caused by key-sharing, QUs cannot learn DO’s key, (3) query privacy against CS and DO: the privacy of query points is preserved as well, (4) query controllability: QUs cannot launch a feasible k-NN query for any new point without approval of DO. We provide theoretical guarantees for security and privacy properties, and show the efficiency of our scheme through extensive experiments.

Quantum Authentication Protocol for Classical Messages Based on Bell states and Hash Function

Quantum authentication protocols can be used to authenticate both quantum messages and classical messages. In this paper, a new quantum authentication protocol of classical messages is proposed. In our protocol, a sequence of Bell states is shared by the message sender and the corresponding receiver. This sequence is used as the authentication key. Four different unitary operations U0, U1, U2 and U3 are used to encode a classical message m and its hash value h(m) into a sequence of Bell states. To authenticate the classical message, the message receiver extracts m and h(m) from the qubits owned by himself/herself, and verifies whether h(m) matches m. The adversary’s disturbance to the quantum channel can be detected by checking whether h(m) matches m. The transmitted message has the properties of both secrecy and authentication. Our quantum authentication protocol is secure against message attack and no-message attack.

Anonymous authentication for circuits from correlation-relaxed two-to-one recoding

Anonymous authentication is very useful to protect the users’ identity privacy in distributed environment, such as in cloud computing. With the anonymous property, attribute-based signature (ABS) provides a cryptographic tool to construct a secure authentication scheme. In an attribute-based signature scheme, a signer owning a set of attributes is able to sign anonymously message with regarding to a policy, while the signatures are kept unforgeable even in the case of collusion. In this paper, we provide a novel construction of key-policy ABS for general circuits. Our construction is based on the correlation-relaxed two-to-one recoding (CR-TOR), which can be instantiated from the existence of multilinear maps. Due to the key indistinguishability property of the CR-TOR system, we prove that our scheme is selective unforgeability in the standard model under the k-Multilinear Computational Diffie-Hellman (k-MCDH) assumption. At the same time, the proposed scheme also holds the property of perfect privacy. Our framework based on CR-TOR leads to a circuit-ABS scheme that is conceptually different from the existing construction in the multilinear maps setting.

Differential privacy preserving clustering using Daubechies-2 wavelet transform

Most of the existing privacy preserving clustering (PPC) algorithms do not consider the worst case privacy guarantees and are based on heuristic notions. In addition, these algorithms do not run efficiently in the case of high dimensionality of data. In this paper, to alleviate these challenges, we propose a new PPC algorithm, which is based on Daubechies-2 wavelet transform (D2WT) and preserves the differential privacy notion. Differential privacy is the strong notion of privacy, which provides the worst case privacy guarantees. On the other hand, most of the existing differential-based PPC algorithms generate data with poor utility. If we apply differential privacy properties over the original raw data, the resulting data will offer lower quality of clustering (QOC) during the clustering analysis. Therefore, we use D2WT for the preprocessing of the original data before adding noise to the data. By applying D2WT to the original data, the resulting data not only contains lower dimension compared to the original data, but also can provide differential privacy guarantee with high QOC due to less noise addition. The proposed algorithm has been implemented and experimented over some well-known datasets. We also compare the proposed algorithm with some recently introduced algorithms based on utility and privacy degrees.

K-jump: A strategy to design publicly-known algorithms for privacy preserving micro-data disclosure

Data owners are expected to disclose micro-data for research, analysis, and various other purposes. In disclosing micro-data with sensitive attributes, the goal is usually two fold. First, the data utility of disclosed data should be maximized for analysis purposes. Second, the private information contained in such data must be to an acceptable level. Typically, a disclosure algorithm evaluates potential generalization functions in a predetermined order, and then discloses the first generalization that satisfies the desired privacy property. Recent studies show that adversarial inferences using knowledge about such disclosure algorithms can usually render the algorithm unsafe. In this paper, we show that an existing unsafe algorithm can be transformed into a large family of safe algorithms, namely, k-jump algorithms. We then prove that the data utility of different k-jump algorithms is generally incomparable. The comparison of data utility is independent of utility measures and syntactic privacy models. Finally, we analyze the computational complexity of k-jump algorithms, and confirm the necessity of safe algorithms even when a secret choice is made among algorithms.

A Secure Code-Based Authentication Scheme for RFID Systems

Two essential problems are still posed in terms of Radio Frequency Identification (RFID) systems, including: security and limitation of resources. Recently, Li et al.'s proposed a mutual authentication scheme for RFID systems in 2014, it is based on Quasi CyclicModerate Density Parity Check (QC-MDPC) McEliece cryptosystem. This cryptosystem is designed to reducing the key sizes. In this paper, we found that this scheme does not provide untraceability and forward secrecy properties. Furthermore, we propose an improved version of this scheme to eliminate existing vulnerabilities of studied scheme. It is based on the QC-MDPC McEliece cryptosystem with padding the plaintext by a random bitstring. Our work also includes a security comparison between our improved scheme and different code-based RFID authentication schemes. We prove secrecy and mutual authentication properties by AVISPA (Automated Validation of Internet Security Protocols and Applications) tools. Concerning the performance, our scheme is suitable for low-cost tags with resource limitation.

Styx: Privacy risk communication for the Android smartphone platform based on apps' data-access behavior patterns

Modern smartphone platforms offer a multitude of useful features to their users but at the same time they are highly privacy affecting. However, smartphone platforms are not effective in properly communicating privacy risks to their users. Furthermore, common privacy risk communication approaches in smartphone app ecosystems do not consider the actual data-access behavior of individual apps in their risk assessments. Beyond privacy risks such as the leakage of single information (first-order privacy risk), we argue that privacy risk assessments and risk communication should also consider threats to user privacy coming from user-profiling and data-mining capabilities based on the long-term data-access behavior of apps (second-order privacy risk). In this paper, we introduce Styx, a novel privacy risk communication system for Android that provides users with privacy risk information based on the second-order privacy risk perspective. We discuss results from an experimental evaluation of Styx regarding its effectiveness in risk communication and its effects on user perceptions such as privacy concerns and the trustworthiness of a smartphone. Our results suggest that privacy risk information provided by Styx improves the comprehensibility of privacy risk information and helps the users in comparing different apps regarding their privacy properties. The results further suggest that an improved privacy risk communication on smartphones can increase trust towards a smartphone and reduce privacy concern.

Anonymous protocols: Notions and equivalence

Privacy protection has become a major issue in modern societies. Many efforts have been provided in the last years to catch properly the requirements that cryptographic primitives and low-level protocols should meet in order to be useful for building privacy-preserving applications. In particular, anonymity is an important property to achieve, and the notion of key privacy in public-key encryption, which guarantees that an adversary is unable to tell with which public key a certain ciphertext has been produced, plays a key-role in the design of anonymous protocols.Secret sets and anonymous broadcast encryption are two examples of useful anonymous protocols. A secret set is a representation of a subset of users of a given universe satisfying some basic membership privacy properties, and anonymous broadcast encryption is a mechanism to encrypt a broadcast message that only authorized users, whose identities are kept secret, can decrypt.In this paper we show that, even if apparently the key privacy property of an encryption scheme seems to be unrelated to the security of the encrypted content, and it looks like just an additional property the encryption scheme can enjoy, for a robust encryption scheme key privacy under chosen ciphertext attack implies non-malleability and, hence, security under chosen ciphertext attacks. This result helps to simplify the set of requirements that public key encryption schemes need to satisfy when stating and proving theorems regarding anonymous protocols in which the encryption schemes are used.Then, we provide a formal model for both secret sets and anonymous broadcast encryption and we prove that they are equivalent with respect to non-adaptive adversaries: the former can be used to design the latter and vice versa.Finally, we revisit some previous constructions for secret sets, and we analyze the security properties they enjoy within our adversarial model.