Strong Secrecy Research Articles

E-Trust and E-Service Quality on E-Loyalty: Role of E-Satisfaction and Customer Privacy

Main objective: investigate effect ET and ESQ on ELOY through ESAT and effect ESAT on ELOY moderated privacy. Background Problem: E-Trust, E-service quality and weak privacy of Shopee customers still need to be improved because it will affect the decline in E-loyalty. Novelty: the importance of strengthening the privacy system that is difficult to hack in the Shopee application so that customer privacy is truly protected from cyber hacking. Research Methods: samples of 265 Shopee customers in Central Java Indonesia, PLS-SEM used for data analyzing. Results: ET and ESQ have a positive link to ESAT; the effect of ET, ESQ and ESAT on ELOY is also positive. ET and ESQ affect ELOY through ESAT; the effect of ESAT on ELOY can be moderated by privacy. Conclusion: E-Satisfaction is effective as an intermediary variable for E-Trust and E-Servqual in influencing E-Loyalty of Shopee customers and privacy is effective as a moderating variable in the influence of E-Satisfaction on E-Loyalty.

New private information retrieval codes for imperfect privacy

In this paper, we introduce new codes for the private information retrieval (PIR). The conventional Tian-Sun-Chen (TSC) scheme achieves PIR capacity under the perfect privacy conditions with the minimum message length. Based on the TSC scheme, weakly PIR (WPIR) schemes have been developed to benefit download costs by relaxing privacy. We show that the proposed scheme has lower download cost than the TSC scheme in the weak privacy setting. While our scheme is not capacity-achieving under perfect privacy, it has smaller message sizes compared with the TSC scheme.

Secrecy culture, client importance, and auditor reporting behavior: an international study

PurposeThis study aims to investigate whether auditors compromise their independence for economically important clients in countries with a secrecy culture.Design/methodology/approachThe authors empirically examine the research question based on a data set of 33 countries for the period from 1995 to 2018. The dependent variable is the auditors’ propensity to issue modified audit opinions, which is a proxy for auditor independence. The authors use relative client size as a proxy for client importance. The authors adopt the Heckman (1979) two-stage model to mitigate the potential endogeneity issue involved in the selection of Big-N auditors.FindingsUsing a large sample of firms and controlling for the firm- and country/region-level factors, this study reveals that both Big-N and non-Big-N auditors are more likely to issue modified audit opinions to clients located in countries with a strong secrecy culture relative to those located in other countries. However, Big-N auditors are more likely to issue modified audit opinions for their economically important clients with a secrecy culture relative to their other clients, while no or weaker evidence is found for non-Big-N auditors. The results are consistent and robust to endogeneity tests and sensitivity analyses.Originality/valueThis study enriches the literature by providing a new perspective on auditor independence that an auditor’s reporting behavior can vary depending on the client’s importance and auditor type, even under the same secrecy culture.

A New Achievable Region of the K-User MAC Wiretap Channel with Confidential and Open Messages Under Strong Secrecy

A New Achievable Region of the <i>K</i>-User MAC Wiretap Channel with Confidential and Open Messages Under Strong Secrecy

An anonymous authentication scheme with conditional privacy-preserving for Vehicular Ad hoc Networks based on zero-knowledge proof and Blockchain

Recently, Vehicular Ad hoc Networks (VANETs) have gained extensive attention in both academia and industry. VANETs play a seminal role in smart transportation by enhancing driving convenience and traffic efficiency through real-time information interaction. Despite this, ensuring secure authentication and privacy preservation remains two challenging issues in VANETs. The existence of security schemes that are vulnerable to privacy and security issues or have substantial computation and communication overheads shows the necessity for further research in this area. Therefore, in this article, we present an anonymous authentication scheme based on Zero-Knowledge Proof. To be more specific, we adopted Simulation Extractable Zero-Knowledge SNARKs (SE zk-SNARKs) to achieve anonymity and conditional privacy. Although zk-SNARK proofs are succinct and fast to verify, the time required for proof generation poses a major obstacle in using zk-SNARKs in a time-sensitive VANET environment. To overcome this drawback, we focus on key components of the proof statement, separating the repetitive calculations, and archiving in the Blockchain, which serves as an immutable data storage. By omitting repetitive calculations, our proposed scheme becomes lightweight enough to run efficiently on VANETs. Security analysis showed that the proposed protocol satisfied security and privacy requirements. Additionally, a computation cost analysis is provided to demonstrate the significant advantage of our proposed scheme. ProVerif is used to verify the strong secrecy of the protocol, and the results show that privacy can be guaranteed in the proposed scheme.

FedER: Federated Learning through Experience Replay and privacy-preserving data synthesis

In the medical field, multi-center collaborations are often sought to yield more generalizable findings by leveraging the heterogeneity of patient and clinical data. However, recent privacy regulations hinder the possibility to share data, and consequently, to come up with machine learning-based solutions that support diagnosis and prognosis. Federated learning (FL) aims at sidestepping this limitation by bringing AI-based solutions to data owners and only sharing local AI models, or parts thereof, that need then to be aggregated. However, most of the existing federated learning solutions are still at their infancy and show several shortcomings, from the lack of a reliable and effective aggregation scheme able to retain the knowledge learned locally to weak privacy preservation as real data may be reconstructed from model updates. Furthermore, the majority of these approaches, especially those dealing with medical data, relies on a centralized distributed learning strategy that poses robustness, scalability and trust issues. In this paper we present a federated learning strategy, FedER, that, exploiting experience replay and generative adversarial concepts, effectively integrates features from local nodes, providing models able to generalize across multiple datasets while maintaining privacy. FedER is tested on two tasks — tuberculosis and melanoma classification — using multiple datasets in order to simulate realistic non-i.i.d. medical data scenarios. Results show that our approach achieves performance comparable to standard (non-federated) learning and significantly outperforms state-of-the-art federated methods. Remarkably, we also observe that FedER enables any node model to be used as a global federation model. Indeed, the experience replay strategy with privacy-preserving synthetic data allows all node models to converge to reach the same optimum without the need of a single shared model. Code is available at https://github.com/perceivelab/FedER.

Smoothing of Binary Codes, Uniform Distributions, and Applications.

The action of a noise operator on a code transforms it into a distribution on the respective space. Some common examples from information theory include Bernoulli noise acting on a code in the Hamming space and Gaussian noise acting on a lattice in the Euclidean space. We aim to characterize the cases when the output distribution is close to the uniform distribution on the space, as measured by the Rényi divergence of order α∈(1,∞]. A version of this question is known as the channel resolvability problem in information theory, and it has implications for security guarantees in wiretap channels, error correction, discrepancy, worst-to-average case complexity reductions, and many other problems. Our work quantifies the requirements for asymptotic uniformity (perfect smoothing) and identifies explicit code families that achieve it under the action of the Bernoulli and ball noise operators on the code. We derive expressions for the minimum rate of codes required to attain asymptotically perfect smoothing. In proving our results, we leverage recent results from harmonic analysis of functions on the Hamming space. Another result pertains to the use of code families in Wyner's transmission scheme on the binary wiretap channel. We identify explicit families that guarantee strong secrecy when applied in this scheme, showing that nested Reed-Muller codes can transmit messages reliably and securely over a binary symmetric wiretap channel with a positive rate. Finally, we establish a connection between smoothing and error correction in the binary symmetric channel.

Inner-Product Matchmaking Encryption: Bilateral Access Control and Beyond Equality

We present an inner-product matchmaking encryption (IP-ME) scheme achieving weak privacy and authenticity in prime-order groups under symmetric external Diffie–Hellman (SXDH) assumption in the standard model. We further present an IP-ME with Monotone Span Program Authenticity (IP-ME with MSP Auth) scheme, where the chosen sender policy is upgraded to MSP, and the scheme also achieves weak privacy and authenticity in prime-order groups under SXDH assumption in the standard model. Both of the schemes have more expressive functionalities than identity-based matchmaking encryption (IB-ME) scheme, and are simpler than Ateniese et al.’s modular ME scheme (Crypto’ 19). But our schemes only achieve a very limited flavor of security, which is reflected in the privacy.

Geo-indistinguishable masking: enhancing privacy protection in spatial point mapping

ABSTRACT Spatial point mapping is a useful practice in exploratory point pattern analysis, but it poses significant privacy risks as the identity of individuals may be revealed from the maps. Geomasking methods have been developed to mitigate the risks by displacing spatial points before mapping. However, many of these methods rely on a weak privacy notion called spatial k-anonymity, which is insufficient to withstand the growing amount of spatial data (e.g. land use) that adversaries can use as side information to infer the actual locations of individuals. We proposes a method called geo-indistinguishable masking to address this issue by relying on a strong privacy notion called geo-indistinguishability. This notion ensures consistent levels of privacy protection regardless of any side information. The method consists of two steps. The first step involves creating a masking area for each spatial point to include a set of candidate locations to which the point can be relocated. In the second step, we formulate an optimization model to ensure the masked locations satisfy geo-indistinguishability while minimizing the distance displaced. Computational experiments on a synthetic dataset demonstrate that our proposed method is both efficient and effective in providing strong privacy protection while preserving the spatial point patterns.

Counteracting Side Channels in Cross-User Client-Side Deduplicated Cloud Storage

Client-side data deduplication enables cloud storage services to reduce storage-space and bandwidth consumption, resulting in reduced operating cost and a high level of user satisfaction. However, duplicate checks (i.e., the corresponding message exchange) generate a side channel, exposing the file existence status to attackers. In particular, the binary response from a duplicate check reveals file existence information. This can be exploited to launch further attacks, such as learning sensitive file content and establishing a covert channel. As current solutions provide only weaker privacy or rely on unreasonable assumptions, we propose RAndom REsponse (RARE) to achieve stronger privacy. The underlying principle is that the uploading user simultaneously sends a duplication request for two chunks. The cloud receiving the request returns a carefully designed randomized duplication response to preserve the deduplication gain and minimize privacy leakage. By proposing multiple chunk uploading and chunk re-arrangement strategies, we optimize the design of RARE to significantly reduce the communication burden without compromising privacy. We also study the impact of different implementations of multiple chunk uploading on the deduplication gain, further reducing communication cost. The analytical results confirm that formal privacy is ensured, whereas experiment results demonstrate that RARE preserves both privacy and the deduplication benefit.

Explicit Wiretap Channel Codes via Source Coding, Universal Hashing, and Distribution Approximation, When the Channels’ Statistics are Uncertain

We consider wiretap channels with uncertainty on the eavesdropper channel under (i) noisy blockwise type II, (ii) compound, or (iii) arbitrarily varying models. We present explicit wiretap codes that can handle these models in a unified manner and only rely on three primitives, namely source coding with side information, universal hashing, and distribution approximation. Our explicit wiretap codes achieve the best known single-letter achievable rates, previously obtained non-constructively, for the models considered. Our results are obtained for strong secrecy, do not require a pre-shared secret between the legitimate users, and do not require any symmetry properties on the channel. An extension of our results to compound main channels is also derived via new capacity-achieving polar coding schemes for compound settings.

“Green” innovation, privacy regulation and environmental policy

We present a Hotelling duopoly and study the relationship between privacy regulation and environmental policy in an industry in which firms invest in “green” innovation (E-R&D) to reduce emissions in production. This research concludes that governments need to set emission taxes consistent with privacy regulations. With strong privacy protections, emission taxes should be higher than the case with no privacy protections. In the asymmetric case with poor privacy regulation, the optimal emission tax is highest to achieve emissions reductions. In addition, an economic trade-off can arise from the industry profits and E-R&D investment. With weak privacy regulation, the effort of E-R&D is highest, and corporatedo the least harm to the environment, while the industrial profits are lowest due to the massive tax burden. Finally, we suggest that policymakers should (i) combine reasonable emission tax policies and green finance for renewable energy projects, (ii) conduct weak privacy regulations on green finance and renewable energy projects, and (iii) encourage uniform pricing for renewable energy products from a competition policy perspective.

Quadratically Constrained Myopic Adversarial Channels

We study communication in the presence of a jamming adversary where quadratic power constraints are imposed on the transmitter and the jammer. The jamming signal is allowed to be a function of the codebook, and a noncausal but noisy observation of the transmitted codeword. For a certain range of the noise-to-signal ratios (NSRs) of the transmitter and the jammer, we are able to characterize the capacity of this channel under deterministic encoding or stochastic encoding, i.e., with no common randomness between the encoder/decoder pair. For the remaining NSR regimes, we determine the capacity under the assumption of a small amount of common randomness (at most <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\log (n)$ </tex-math></inline-formula> bits in one sub-regime, and at most <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Omega ({n})$ </tex-math></inline-formula> bits in the other sub-regime) available to the encoder-decoder pair. Our proof techniques involve a novel myopic list-decoding result for achievability, and a Plotkin-type push attack for the converse in a subregion of the NSRs, both of which may be of independent interest. We also give bounds on the strong secrecy capacity of this channel assuming that the jammer is simultaneously eavesdropping.

Strong Secrecy of Arbitrarily Varying Wiretap Channel With Constraints

The strong secrecy transmission problem of the arbitrarily varying wiretap channel (AVWC) with input and state constraints is investigated in this paper. First, a stochastic-encoder code lower bound of the strong secrecy capacity is established by applying the type argument and Csiszár’s almost independent coloring lemma. Then, a superposition stochastic-encoder code lower bound of the secrecy capacity is provided. The superposition stochastic-encoder code lower bound can be larger than the ordinary stochastic-encoder code lower bound. Random code lower and upper bounds of the secrecy capacity of the AVWC with constraints are further provided. Based on these results, we further consider a special case of the model, namely severely less noisy AVWC, and give the stochastic-encoder code and random code capacities. It is proved that the stochastic-encoder code capacity of the AVWC with constraints is either equal to or strictly smaller than the corresponding random code capacity, which is consistent with the property of the ordinary AVC. Finally, some numerical examples are presented to better illustrate our capacity results. Compared to the soft covering lemma that requires the codewords to be generated i.i.d., our method has more relaxed requirements regarding codebooks. It is proved that the good codebooks for secure transmission can be generated by choosing codewords randomly from a given type set, which is critical when considering the AVWC with constraints.

Implementation and Performance of Face Recognition Payment System Securely Encrypted by SM4 Algorithm

Face recognition payment is a new type of payment method, with AI face recognition technology as the core, and its speed and convenience are more in line with the users’ payment habits. However, the face is a biological feature with weak privacy, and the protection of user information security is particularly important. At present, face payment technology still has security risks, and the data transmitted during the transaction process are vulnerable to attacks. Aiming at the security problems in the payment process, a payment system that is jointly encrypted by the SM4 algorithm and the face liveness detection algorithm was proposed in this paper, which supports a variety of communication methods. The hardware platform adopts an octa-core 64-bit ARM processor with a main frequency of 1.8 GHz, which has powerful computing and processing capabilities. Based on the Android intelligent operating system, the development environment is more secure and convenient. It is also equipped with a liveness detection 3D structured light camera, which dynamically collects face information and accurately analyzes the characteristics of living bodies. Through the data encryption and decryption test and face performance index detection, the expected effect of the system was achieved, which greatly improved the performance of the face payment system currently studied. The SM4 encryption algorithm improved the running rate of encrypted data and the security of face transaction data transmission, the face detection algorithm improved the accuracy of living body feature recognition, and the payment system effectively improved the accuracy and security of face payment.

When blockchain meets smart grids: A comprehensive survey

Recent years have witnessed an increasing interest in the blockchain technology, and many blockchain-based applications have been developed to take advantage of its decentralization, transparency, fault tolerance, and strong security. In the field of smart grids, a plethora of proposals have emerged to utilize blockchain for augmenting intelligent energy management, energy trading, security and privacy protection, microgrid management, and energy vehicles. Compared with traditional centralized approaches, blockchain-based solutions are able to exploit the advantages of blockchain to realize better functionality in smart grids. However, the blockchain technology itself has its disadvantages in low processing throughput and weak privacy protection. Therefore, it is of paramount importance to study how to integrate blockchain with smart grids in a more effective way so that the advantages of blockchain can be maximized and its disadvantages can be avoided. This article surveys the state-of-the-art solutions aiming to integrate the emergent blockchain technology with smart grids. The goal of this survey is to discuss the necessity of applying blockchain in different components of smart grids, identify the challenges encountered by current solutions, and highlight the frameworks and techniques used to integrate blockchain with smart grids. We also present thorough comparison studies among blockchain-based solutions for smart grids from different perspectives, with the aim to provide insights on integrating blockchain with smart grids for different smart grid management tasks. Finally, we list the current projects and initiatives demonstrating the current effort from the practice side. Additionally, we draw attention to open problems that have not yet been tackled by existing solutions, and point out possible future research directions.

Facial recognition technology for policing and surveillance in the Global South: a call for bans

The use of facial recognition technology (FRT) for policing and surveillance is spreading across Asia, Africa and Latin America. Advocates are saying this technology can solve crimes, locate missing people and prevent terrorist attacks. Yet, as this article argues, deploying FRT for policing and surveillance poses a grave threat to civil society, especially systems to identify or track people without any criminal history. In every political system, this has the potential to deepen discriminatory policing, have a chilling effect on activism and turn everyone into a suspect. The dangers rise exponentially, moreover, in places with inconsistent rule of law, poor human rights records, weak privacy and data laws and authoritarian rulers – traits common across scores of countries now installing FRT. Regulating use is unlikely to prevent these harms, the article contends, given the powerful political and corporate forces in play, given the ways firms push legal limits, exploit loopholes and lobby legislators, and given the tendency over time of surveillance technology to creep across state agencies and into new forms of social control. Calls to ban FRT are growing louder by the day. This article makes the case for why bans are especially necessary in the Global South.

A Provably Secure Identity Based Authenticated Key Agreement Protocol with Multiple PKG Compatibility for Inter-Vehicular Ad hoc Networks

Vehicular Ad hoc Networks (VANETs) facilitate the timely dissemination of road traffic information, including early warnings related to road conditions, weather updates, traffic congestion and collisions. The information exchanged in VANETs is crucial for maintaining uninterrupted traffic flow, minimizing road mishaps, expediting emergency services and analyzing potential detours in case of congestion. In order to ensure the legitimacy of the interchanged messages and to preserve confidentiality, it is essential to establish secure authenticated channels among the communicating automotive nodes. Considering the highly dynamic nature of VANETs and the diversity of the participating vehicular nodes, Identity based Authenticated Key Agreement (ID-AKA) protocol in the multiple Private Key Generator (PKG) setting, is an ideal cryptographic technique for securing inter-vehicular communications. We cryptanalyze the existing pairing-free multiple PKG compatible ID-AKA (mPKG-ID-AKA) protocol designed for securing VANETs and prove that the protocol lacks weak Perfect Forward Secrecy (wPFS). Our investigations also reveal that previous attempts to model mPKG-ID-AKA protocols are computation-intensive and provide weaker security guarantees. To this end, we propose an efficient eCK secure mPKG-ID-AKA protocol based on the gap Diffie-Hellman assumption. Experimental analysis suggests that the proposed protocol attains the highest computation efficiency, when compared to the existing mPKG-ID-AKA schemes and is therefore highly suitable for securing inter-vehicular communications.

DeepClean: A Robust Deep Learning Technique for Autonomous Vehicle Camera Data Privacy

Autonomous Vehicles (AVs) are equipped with several sensors which produce various forms of data, such as geo-location, distance, and camera data. The volume and utility of these data, especially camera data, have contributed to the advancement of high-performance self-driving applications. However, these vehicles and their collected data are prone to several security and privacy attacks. One of the main attacks against the AV-generated camera data is location inference, in which camera data is used to extract knowledge for tracking the users. A few research studies have proposed privacy-preserving approaches for analysing AV-generated camera data using powerful generative models, such as Variational Auto Encoder (VAE) and Generative Adversarial Network (GAN). However, the related work considers a weak geo-localisation attack model, which leads to weak privacy protection against stronger attack models. In this paper, we propose DeepClean, a robust deep learning model that combines VAE and a private clustering technique. DeepClean learns distinct labelled object structures of the image data as a cluster, and generates a more visual representation of the non-private object clusters, e.g., roads. It then distorts the private object areas using a private Gaussian Mixture Model (GMM) to learn distinct cluster structures of the labelled object areas. The synthetic images generated from our model guarantee privacy and resist against a robust location inference attack by less than 4% localisation accuracy. This implies that using DeepClean for synthetic data generation, it is less likely for a subject to be localised by an attacker even when using a robust geo-localisation attack. The overall image utility level of the generated synthetic images by DeepClean is comparable to the benchmark studies.

Network Information Theoretic Security With Omnipresent Eavesdropping

Shannon showed that to achieve perfect secrecy in point-to-point communication, the message rate cannot exceed the shared secret key rate giving rise to the simple one-time pad encryption scheme. In this paper, we extend this work from point-to-point to networks. We consider a connected network with pairwise communication between the nodes and assume that each node is provided with a certain amount of secret bits before communication commences. An eavesdropper with unlimited computing power has access to all communication and can hack a subset of the nodes not known to the rest of the nodes. We investigate the limits on information-theoretic secure communication with end-to-end encryption for this network. We establish a tradeoff between the secure channel rate (for a node pair) and the secure network rate (sum over all node pair rates) and show that information-theoretic secrecy can be achieved asymptotically if and only if the sum rate of any subset of unhacked channels does not exceed the shared unhacked-secret-bit rate of these channels. We also propose a practical scheme that achieves a good balance of network and channel rates with information-theoretic secrecy guarantee. This work has a wide range of potential applications for which strong secrecy is desired, such as cyber-physical systems, distributed-control systems, and ad-hoc networks.