Secure signal processing: Privacy preserving cryptographic protocols for multimedia

Recent advances in technology provided a suitable environment for the people in which they can benefit from online services in their daily lives. Despite several advantages, online services also constitute serious privacy risks for their users as the main input to algorithms are privacy sensitive such as demographic information, shopping patterns, medical records, etc. While traditional security mechanisms can eliminate a number of attacks from outside, these mechanisms can not protect the privacy of the users as the service provider itself constitutes the biggest potential risk. In this thesis, we focus on principled solutions to protect the privacy of users in multimedia applications. For this purpose we propose to keep the privacy-sensitive data safe by means of encryption during processing. This approach eliminates the risk of possible privacy abuse as the sensitive data is only available to the owner but no other party. However, once encrypted, the structure in data is destroyed as a consequence of the encryption procedure and thus we need appropriate tools to process encrypted data. Therefore, we focus on a number of cryptographic tools such as homomorphic encryption schemes and multiparty computation (MPC) techniques to realize privacy-preserving multimedia applications. The proposed principled solutions consider the signal processing aspect of the multimedia applications which is a new idea to the best of our knowledge. In particular, we focus on a number of prototypical applications namely, face detection, user clustering in a social network, recommendation generation and anonymous fingerprinting. Based on these selected applications, we addressed the major challenges for secure signal processing: data representation, data expansion, realizing linear and non-linear operations and efficiency of the proposed protocols in terms of communication and computational costs. We propose to scale and round the signal values prior to encryption as these operations are highly inefficient to be realized in the encrypted domain. Moreover, we reserve sufficient space in terms of bit length for each signal sample to accommodate the possible expansion in bit size in the subsequent processing steps. However, reserving more bits for signals does not contradict with the data expansion problem. As the cipher text space is much larger than the size of the original -- and even scaled -- signal samples, data expansion after encryption increases data transmission and storage costs significantly. In order to minimize the cost we propose to pack a number of signal samples in one encryption and process them when they are in the packed form. This approach requires cryptographic protocols particularly designed for the packed data but in the end saves considerable resources regarding bandwidth and storage capacity, even computational power. Homomorphism plays a crucial role in our proposed solutions. With the help of homomorphic encryption, we are able to implement linear operations such as correlation and projection without interaction. However, linear operations are only a part of the signal processing. For the non-linear operations like distance computation, thresholding and comparison, we exploit MPC techniques. These techniques are often interactive and computationally expensive compared to the original systems in plain. However, by using data packing and designing the protocols with care, the communication and computational costs were reduced significantly. In this thesis, we have shown that preserving privacy for multimedia signal processing is feasible. We determined the major challenges of secure signal processing and combined a set of cryptographic tools successfully with signal processing to realize the applications in the encrypted domain. The proposed solutions demonstrate that the privacy concerns in multimedia signal processing applications can be coped with by using cryptographic tools. Moreover, protocols that are designed to realize certain operations in the encrypted domain can be used in other applications and settings with a number of modifications.MediamaticsElectrical Engineering, Mathematics and Computer Scienc

Privacy-Preserving Data Aggregation with Probabilistic Range Validation

Privacy-preserving data aggregation protocols have been researched widely, but usually cannot guarantee correctness of the aggregate if users are malicious. These protocols can be extended with zero-knowledge proofs and commitments to work in the malicious model, but this incurs a significant computational cost on the end users, making adoption of these protocols less likely. We propose a privacy-preserving data aggregation protocol for calculating the sum of user inputs. Our protocol gives the aggregator confidence that all inputs are within a desired range. Instead of zero-knowledge proofs, our protocol relies on a probabilistic hypergraph-based detection algorithm with which the aggregator can quickly pinpoint malicious users. Furthermore, our protocol is robust to user dropouts and, apart from the setup phase, it is non-interactive.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Cyber Securit

Efficient Circuits for Permuting and Mapping Packed Values Across Leveled Homomorphic Ciphertexts

Cloud services are an essential part of our digital infrastructure as organizations outsource large amounts of data storage and computations. While organizations typically keep sensitive data in encrypted form at rest, they decrypt it when performing computations, leaving the cloud provider free to observe the data. Unfortunately, access to raw data creates privacy risks. To alleviate these risks, researchers have developed secure outsourced data processing techniques. Such techniques enable cloud services that keep sensitive data encrypted, even during computations. For this purpose, fully homomorphic encryption is particularly promising, but operations on ciphertexts are computationally demanding. Therefore, modern fully homomorphic cryptosystems use packing techniques to store and process multiple values within a single ciphertext. However, a problem arises when packed data in one ciphertext does not align with another. For this reason, we propose a method to construct circuits that perform arbitrary permutations and mappings of such packed values. Unlike existing work, our method supports moving values across multiple ciphertexts, considering that the values in real-world scenarios cannot all be packed within a single ciphertext. We compare our open-source implementation against the state-of-the-art method implemented in HElib, which we adjusted to work with multiple ciphertexts. When data is spread among five or more ciphertexts, our method outperforms the existing method by more than an order of magnitude. Even when we only consider a permutation within a single ciphertext, our method still outperforms the state-of-the-art works implemented by HElib for circuits of similar depth.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Cyber Securit

Privacy-Preserving Bin-Packing With Differential Privacy

With the emerging of e-commerce, package theft is at a high level: It is reported that 1.7 million packages are stolen or lost every day in the U.S. in 2020, which costs $25 million every day for the lost packages and the service. Information leakage during transportation is an important reason for theft since thieves can identify which truck is the target that contains the valuable products. In this paper, we address the privacy and security issues in bin-packing, which is an algorithm used in delivery centers to determine which packages should be loaded together to a certain truck. Data such as the weight of the packages is needed when assigning items into trucks, which can be called bins. However, the information is sensitive and can be used to identify the contents in the package. To provide security and privacy during bin-packing, we propose two different privacy-preserving data publishing methods. Both approaches use differential privacy (DP) to hide the existence of any specific package to prevent it from being identified by malicious users. The first approach combines differential privacy with k-anonymity, and the other one applies clustering before differential privacy. Our extensive analyses and experimental results clearly show that our proposed approaches have better privacy guarantees, better efficiency, and better performance than the existing works that use either differential privacy or k-anonymity.Cyber Securit

Anonymous Fingerprinting with Robust QIM Watermarking Techniques

Fingerprinting is an essential tool to shun legal buyers of digital content from illegal redistribution. In fingerprinting schemes, the merchant embeds the buyer's identity as a watermark into the content so that the merchant can retrieve the buyer's identity when he encounters a redistributed copy. To prevent the merchant from dishonestly embedding the buyer's identity multiple times, it is essential for the fingerprinting scheme to be anonymous. Kuribayashi and Tanaka, 2005, proposed an anonymous fingerprinting scheme based on a homomorphic additive encryption scheme, which uses basic quantization index modulation (QIM) for embedding. In order, for this scheme, to provide sufficient security to the merchant, the buyer must be unable to remove the fingerprint without significantly degrading the purchased digital content. Unfortunately, QIM watermarks can be removed by simple attacks like amplitude scaling. Furthermore, the embedding positions can be retrieved by a single buyer, allowing for a locally targeted attack. In this paper, we use robust watermarking techniques within the anonymous fingerprinting approach proposed by Kuribayashi and Tanaka. We show that the properties of an additive homomorphic cryptosystem allow for creating anonymous fingerprinting schemes based on distortion compensated QIM (DC-QIM) and rational dither modulation (RDM), improving the robustness of the embedded fingerprints. We evaluate the performance of the proposed anonymous fingerprinting schemes under additive-noise and amplitude-scaling attacks.Electrical Engineering, Mathematics and Computer Scienc

Privacy-Preserving Equality Test

Many countries around the globe are investing on e-healthcare increasingly, which offers tremendous benefits to all stakeholders in healthcare. Nevertheless, this technology introduces unprecedented privacy concerns toward patients and raise more uncertainty among them to use e-healthcare for monitoring their vital signs. These concerns necessitate finding scientific solutions, which enable e-healthcare systems to process and analyze privacy-sensitive information, and offer services to the patients without violating their privacy. One of the approaches to address the privacy concerns is utilizing cryptographic techniques, which provide us tools to create Privacy-by-Design e-healthcare systems. Moreover, cryptographic solutions allow to process patients’ private information, while they are kept confidential and only known to the patients. Although using cryptographic technique is effective in providing privacy and processing private information, it results in high computational and communicational overhead. In fact, the current cryptographic building-blocks are not efficient enough for processing encrypted data in large-scale databases. In this paper, we address one of the highly used cryptographic building-blocks, which is checking the equality of two encrypted values. We investigate through the performance of the state-of-the-art secure equality tests and propose novel techniques to reduce their costs in terms of computation and communication. Then, through the complexity analysis and experimental results, we show 99% improvements in terms of computation is achieved. These improvements make the e-healthcare systems more attractive in terms of efficiency and in reach of practical applicability.Cyber SecurityIntelligent System

How to profit from payments channels

Payment channel networks like Bitcoin’s Lightning network are an auspicious approach for realizing high transaction throughput and almost-instant confirmations in blockchain networks. However, the ability to successfully conduct payments in such networks relies on the willingness of participants to lock collateral in the network. In Lightning, the key financial incentive to lock collateral are low fees for routing payments of other participants. While users can choose these fees, real-world data indicates that they mainly stick to default fees. By providing insights on beneficial choices for fees, we aim to incentivize users to lock more collateral and improve the effectiveness of the network. In this paper, we consider a node that given the network topology and the channel details establishes channels and chooses fees to maximize its financial gain. Our contributions are i) formalization of the optimization problem, ii) proving that the problem is NP-hard, and iii) designing and evaluating a greedy algorithm to approximate the optimal solution. In each step, our greedy algorithm establishes a channel that maximizes the increase to ’s total reward, which corresponds to maximizing the number of shortest paths passing through. Our simulation study leveraged real-world data sets to quantify the impact of our gain optimization and indicates that our strategy is at least a factor two better than other strategies.Coronavirus update: Authors whose travel is disrupted can arrange to give video presentationsDistributed SystemsCyber Securit

Privacy-Preserving Collection and Retrieval of Medical Wearables Data

In recent years we have seen a rise in the amount of fitness tracking and self monitoring devices. These devices which often work in conjunction with a smartphone are becoming more accurate and are becoming widely adopted. This trend goes hand in hand with Electronic Health Care (e-health): the shift of health care to the digital domain. E-health would allow patients to measure their medical condition at home, allowing a diagnosis to be made based on measurements taken over a longer period of time, while reducing the work performed by a doctor. Measurements are  tored in the cloud, simplifying the way in which they can be shared with healthcare providers and possibly research  nstitutions. Modernizing healthcare this way should give the patient more insight and control over his/her healthcare and  medical data. Furthermore the amount of visits required to the hospital can be reduced, an endeavor which can be demanding for many less fit for elderly individuals.However, handling medical data this way causes concern for privacy. Often the data handled by these devices is very  sensitive and could easily be used to identify the user and monitor many of their behaviours. In order to achieve privacy there are several approaches. One way is to enforce involved parties through legislation to use the data for specific purposes only. However, this relies on the party being semi-trusted and does not guarantee safety in case of a data-breach. In this work the way in which the integration of wearables into the medical domain is currently taking place and how privacy and security is handled will be explored. Furthermore we will show the current state of research regarding improving this security. Cyber Securit

Decentralized Private Freight Declaration & Tracking with Data Validation

In January 2017, a truck crossed the border between Spain and France for the first time using an e-CMR: An electronic version of the primary transport document required for inter-European logistics. Since that crossing, researchers and logistic organizations have proposed a large number of ideas to further digitize Europe’s supply chain. Many of these ideas involve blockchains, but not all of them validate the data that is posted to them. As a result, participants can make illegitimate claims: Even though the blockchain enables transparency and immutability of the data stores, it does not ensure veracity. We provide several examples of works about information sharing in the supply chain that do not perform such validation. One work that does use the blockchain’s validation functionality is DEFEND. DEFEND addresses customs agencies’ lack of information for international freight inspection by tracking shipping containers throughout their journey. As containers pass from one operator to another, the blockchain participants ensure that containers are not doubly spent. In this work, we propose an extension of DEFEND, in which we further extend the capabilities for validation. Moreover, we provide actual cryptographic protocols to preserve participants’ privacy while DEFEND only described privacy on a high level. Finally, by making a more fine-grained distinction between different actors in the chain, we model the entire supply chain from buyer to seller. As a result, the buyer and seller can now track the respective package’s whereabouts through each leg of its journey.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Cyber Securit

An efficient privacy-preserving comparison protocol in smart metering systems

In smart grids, providing power consumption statistics to the customers and generating recommendations for managing electrical devices are considered to be effective methods that can help to reduce energy consumption. Unfortunately, providing power consumption statistics and generating recommendations rely on highly privacy-sensitive smart meter consumption data. From the past experience, we see that it is essential to find scientific solutions that enable the utility providers to provide such services for their customers without damaging customers’ privacy. One effective approach relies on cryptography, where sensitive data is only given in the encrypted form to the utility provider and is processed under encryption without leaking content. The proposed solutions using this approach are very effective for privacy protection but very expensive in terms of computation and communication. In this paper, we focus on an essential operation for designing a privacy-preserving recommender system for smart grids, namely comparison, that takes two encrypted values and outputs which one is greater than the other one. We improve the state-of-the-art comparison protocol based on Homomorphic Encryption in terms of computation and communication by 56 and 25 %, respectively, by introducing algorithmic changes and data packing. As the smart meters are very limited devices, the overall improvement achieved is promising for the future deployment of such cryptographic protocols for enabling privacy enhanced services in smart grids.Cyber Securit