Searchable Symmetric Encryption (SSE) has opened up an attractive avenue for privacy-preserved processing of outsourced data on the untrusted cloud infrastructure. SSE aims to support efficient Boolean query processing with optimal storage and search overhead over large real databases. However, current constructions in the literature lack the support for multi-client search and dynamic updates to the encrypted databases, which are essential requirements for the widespread deployment of SSE on real cloud infrastructures. Trivially extending a state-of-the-art single client dynamic construction, such as ODXT (Patranabis et al., NDSS'21), incurs significant leakage that renders such extension insecure in practice. Currently, no SSE construction in the literature offers efficient multi-client query processing and search with dynamic updates over large real databases while maintaining a benign leakage profile.

Modern approaches for multiparty secure collaboration must strike the right balance between rich analytics and requisite data privacy guarantees, especially in the face of new regulations.While cryptographic technologies such as fully homomorphic encryption (FHE) and secure multiparty computation (MPC) provide strong, provable security guarantees as standalone tools, deploying them in practice throws up a myriad of challenges, including usability constraints and lack of precise specification of privacy guarantees.In this work, we propose a novel framework for real-world deployment of cryptographic privacy preserving techniques that achieves the twin goals of practical usability in real-world setting and provable privacy guarantees from users' perspective.To this end, we formalize the notion of a secure computation platform (SCP) for privacy preserving data collaboration, and introduce a model for precise specification of privacy guarantees for multiparty workflows.We then describe abstractions of a set of cryptoprimitives, that are usable by non-experts in cryptography.We present two demo workflows that empirically validate our claims, and serve as potential building blocks for the development of squeaky-clean data rooms with practical performance and privacy guarantees.

RAM (random access memory) is an important primitive in verifiable computation. In this paper, we focus on realizing RAM with efficient batching property, i.e, proving a batch of m updates on a RAM of size N while incurring a cost that is sublinear in N. Classical approaches based on Merkle-trees or address ordered transcripts to model RAM correctness are either concretely inefficient, or incur linear overhead in the size of the RAM. Recent works explore cryptographic accumulators based on unknown-order groups (RSA, class-groups) to model the RAM state. While recent RSA accumulator based approaches offer significant improvement over classical methods, they incur linear overhead in the size of the accumulated set to compute witnesses, as well as prohibitive constant overheads.

Secure multiparty computation (MPC) enables privacy-preserving collaborative computation over sensitive data held by multiple mutually distrusting parties. Unfortunately, in the most natural setting where a majority of the parties are maliciously corrupt (also called the dishonest majority setting), traditional MPC protocols incur high overheads and offer weaker security guarantees than are desirable for practical applications. In this paper, we explore the possibility of circumventing these drawbacks and achieving practically efficient dishonest majority MPC protocols with strong security guarantees by assuming an additional semi-honest, non-colluding helper party HP .1We believe that this is a more realistic alternative to assuming an honest majority, since many real-world applications of MPC involving potentially large numbers of parties (such as dark pools) are typically enabled by a central governing entity that can be modeled as the HP.In the above model, we are the first to design, implement and benchmark a practically-efficient and general multi-party framework, Asterisk. Our framework requires invoking HP only a constant number of times, achieves the strong security guarantee of fairness (either all parties learn the output or none do), scales to hundreds of parties, outperforms all existing dishonest majority MPC protocols, and is, in fact, competitive with state-of-the-art honest majority MPC protocols. Our experiments show that Asterisk achieves 228 – 288× speedup in preprocessing as compared to the best dishonest majority MPC protocol. With respect to online time, Asterisk supports 100-party evaluation of a circuit with 106multiplication gates in approximately 20 seconds. We also implement and benchmark practically efficient and highly scalable dark pool instances using Asterisk. The corresponding run times showcase the effectiveness of Asterisk in enabling efficient realizations of real-world privacy-preserving applications with strong security guarantees.

This paper initiates a new direction in the design and analysis of searchable symmetric encryption (SSE) schemes. We provide the first comprehensive security model and definition for SSE that takes into account leakage from the entirety of the SSE system, including not only from access to encrypted indices but also from access to the encrypted database documents themselves. Such system-wide leakage is intrinsic in end-to-end SSE systems, and can be used to break almost all state-of-the-art SSE schemes (Gui et al., IEEE S&P 2023). We then provide a static SSE construction meeting our new security notion. The proposed SSE scheme involves a combination of novel techniques: bucketization to hide volumes of responses to queries, and delayed, pseudorandom write-backs to disrupt access pattern. Our implementation and analysis of the proposed scheme demonstrates that it offers very strong security against general classes of (system-wide) leakage-abuse attacks with moderate overhead. Our scheme scales smoothly to databases containing hundreds of thousand of documents and millions of keyword-document pairs. To the best of our knowledge, this is the first end-to-end SSE scheme that effectively suppresses system-wide leakage while maintaining practical efficiency.

Searchable Symmetric Encryption (SSE) supports efficient keyword searches over encrypted outsourced document collections while minimizing information leakage. All practically efficient SSE schemes supporting conjunctive queries rely crucially on quantum-broken cryptographic assumptions (such as discrete-log hard groups) to achieve compact storage and fast query processing. On the other hand, quantum-safe SSE schemes based on purely symmetric-key cryptoprimitives either do not support conjunctive searches, or are practically inefficient. In particular, there exists no quantum-safe yet practically efficient conjunctive SSE scheme from lattice-based hardness assumptions.We solve this open question by proposing Oblivious Post-Quantum Secure Cross Tags (OQXT) – the first lattice-based practically efficient and highly scalable conjunctive SSE scheme. The technical centerpiece of OQXT is a novel oblivious cross-tag generation protocol with provable security guarantees derived from lattice-based hardness assumptions. We prove the post-quantum simulation security of OQXT with respect to a rigorously defined and thoroughly analyzed leakage profile. We then present a prototype implementation of OQXT and experimentally validate its practical efficiency and scalability over extremely large real-world databases. Our experiments show that OQXT has competitive end-to-end search latency when compared with the best (quantum-broken) conjunctive SSE schemes.

Symmetric Searchable Encryption (SSE) schemes enable keyword searches over encrypted documents. To obtain efficiency, SSE schemes incur a certain amount of leakage. The vast majority of the literature on SSE considers only leakage from one component of the overall SSE system, the encrypted search index. This component is used to identify which documents to return in response to a keyword query. The actual fetching of the documents is left to another component, usually left unspecified in the literature, but generally envisioned as a simple storage system matching document identifiers to encrypted documents.This raises the question: do SSE schemes actually protect the security of data and queries when considered from a system-wide viewpoint? We answer this question in the negative. We do this by introducing a new inference attack that achieves practically efficient, highly scalable, accurate query reconstruction against end-to-end SSE systems. In particular, our attack works even when the SSE schemes are built in the natural way using the state-of-the-art techniques (namely, volume-hiding encrypted multi-maps) designed to suppress leakage and protect against previous generations of attack.A second question is whether the state-of-the-art leakage suppression techniques can instead be applied on a system-wide basis, to protect both the encrypted search index and the encrypted document store, to produce efficient SSE systems. We also answer this question in the negative. To do so, we implement SSE systems using those state-of-the-art leakage suppression methods, and evaluate their performance. We show that storage overheads range from 100× to 800× while bandwidth overheads range from 20× to100×, as compared to a naïve baseline system.Our results motivate the design of new SSE systems that are designed with system-wide security in mind from the outset. In this regard, we show that one such SSE system due to Chen et al. (IEEE INFOCOM 2018), with provable security guarantees based on differential privacy, is also vulnerable to our new attack.In totality, our results force a re-evaluation of how to build end-to-end SSE systems that offer both security and efficiency.

Searchable Symmetric Encryption (SSE) supports efficient yet secure query processing over outsourced symmetrically encrypted databases without the need for decryption. A longstanding open question has been the following: can we design a fast, scalable, linear storage and low-leakage SSE scheme that efficiently supports arbitrary Boolean queries over encrypted databases? In this paper, we present the design, analysis and prototype implementation of the first SSE scheme that efficiently supports conjunctive, disjunctive and more general Boolean queries (in both the conjunctive and disjunctive normal forms) while scaling smoothly to extremely large encrypted databases, and while incurring linear storage overheads and supporting extremely fast query processing in practice. We quantify the leakage of our proposal via a rigorous cryptographic analysis and argue that it achieves security against a well-known class of leakage-abuse and volume analysis attacks. Finally, we demonstrate the storage-efficiency and scalability of our proposed scheme by presenting experimental results of a prototype implementation of our scheme over large real-world databases.

Searchable symmetric encryption (SSE) is a special class of encryption schemes for computing directly over encrypted data. SSE aims to be significantly more efficient as compared to other solutions, such as fully homomorphic encryption (FHE), while leaking only minimal information to the adversary. SSE is particularly efficient and scalable for Boolean queries over large encrypted relational databases outsourced to third-party cloud service providers. However, practical implementations of SSE often suffer from performance bottlenecks due to randomised memory accesses for reads/writes and computation-intensive cryptographic operations. As a result, a gap exists today between theoretically efficient SSE algorithms and practically efficient SSE systems for real-world databases. In this paper, we address this longstanding open question that has otherwise hindered the widespread deployment of SSE over real cloud computing platforms. We proposeCAMiSE–a fully associative memory-integrated framework for designing SSE systems with fast query processing over extremely large databases. We show a novel usage of custom-designed Content Addressable Memory (CAM), together with robust data access policies, to bridge the memory wall in traditional SSE implementations by minimising storage-access latencies due to randomised look-up operations during searches. Coupled with dedicated hardware accelerators for cryptographic operations,CAMiSEachieves extremely fast and scalable query processing over encrypted relational databases. We prototype multiple well-known SSE algorithms and SSE data structures within our proposedCAMiSEframework. Our experiments show that these implementations achieve around$5\times $to$7\times $speed-up over traditional software-based implementations while scaling smoothly to extensive real-world databases with millions of records.

Commitment schemes are one of the basic building blocks to construct secure protocols for multi party computation. Many recent works are exploring hardware primitives like physically unclonable functions to build keyless cryptographic protocols, with minimal assumptions. The asymmetric nature and non-invertibility property of PUFs are widely exploited to build oblivious transfer protocols that are extended to build bit-commitment schemes. However, these schemes require the physical transfer of the PUF device between the interacting parties. In this work, we introduce a new class of hardware-based primitives called physically related functions that enable hardware circuits to securely communicate with each other over insecure channels. We propose a bit-commitment protocol based on this hardware primitive without needing any physical transfer. Our scheme is statistically hiding and computationally binding, requiring only one round of communication while being practically deployable. We explore the security properties of physically related functions, under which we prove the security of our scheme. We experimentally show that it is impossible to break the security of the scheme with more than negligible probability.

Searchable symmetric encryption (SSE) aims to support efficient query-execution directly over encrypted databases. Practical implementations of SSE suffer from performance bottlenecks due to randomised memory accesses and computation-intensive cryptographic operations. We propose CAMISE – a fully associative memory-integrated framework for designing SSE systems with fast query processing over large databases. We show a novel usage of custom-designed Content Addressable Memory (CAM) to minimise storage-access latencies during query execution in SSE systems. We prototype a well-known SSE scheme, namely Oblivious Cross Tags (OXT), within this framework. Our implementation achieves 5x-7x speed-up over traditional software-based implementations while scaling smoothly to real-world databases with millions of records.

Pairing cryptosystems are extremely powerful mathematical tools for developing cryptographic protocols that can provide end-to-end security for applications like Internet-of-Things (IoT), cloud services and cyber-physical systems (CPS). However, these applications require the implementations to be light-weight but still real-time, with the additional feature of being flexible. The flexibility can come from different choices of underlying algorithms along with suitable parameter choices. A software implementation offers better flexibility but lacks in timing performance, whereas custom hardware delivers better performance but has poor flexibility. Furthermore, the designs over small characteristic curves are now insecure against recent attacks. Existing designs do not address the drawback of less flexibility and huge resource consumption collectively. In this article, we present a micro-program controlled hardware design which has the least resource consumption among the similar existing designs on FPGA that offer such programmability and flexibility. This redundant number arithmetic-based architecture consumes only 2506 slices on Xilinx Virtex-7 FPGA. It can be migrated to other device families or updated for different algorithms without data-path or control-path modification. To enhance the flexibility, we developed a custom assembly-like finite state machine (FSM) description, called Prism, and necessary tool to generate the micro-program states. To illustrate the functionality of Prism, we present designs for Tate and Optimal-Ate pairing with the micro-program states generated using this tool.

In the energy sector, IoT manifests in the form of next-generation power grids that provide enhanced electrical stability, efficient power distribution, and utilization. The primary feature of a Smart Grid is the presence of an advanced bi-directional communication network between the Smart meters at the consumer end and the servers at the Utility Operators. Smart meters are broadly vulnerable to attacks on communication and physical systems. We propose a secure and operationally asymmetric mutual authentication and key-exchange protocol for secure communication. Our protocol balances security and efficiency, delegates complex cryptographic operations to the resource-equipped servers, and carefully manages the workload on the resource-constrained Smart meter nodes using unconventional lightweight primitives such as Physically Unclonable Functions. We prove the security of the protocol using well-established cryptographic assumptions. We implement the proposed scheme end-to-end in a Smart meter prototype using commercial-off-the-shelf products, a Utility server, and a credential generator as the trusted third party. Additionally, we demonstrate a physics-based attack named load modification attack on the Smart meter to demonstrate that merely securing the communication channel using authentication does not secure the meter, but requires further protections to ensure the correctness of the reported consumption. Hence, we propose a countermeasure to such an attack that goes side-by-side with our protocol implementation.

This paper initiates the study of “Cryptophasia in Hardware” – a phenomenon that allows hardware circuits/devices with no pre-established secret keys to securely exchange secret information over insecure communication networks. The study of cryptophasia is motivated by the need to establish secure communication channels between lightweight resource-constrained devices incapable of securely storing cryptographic keys and/or executing resource-intensive cryptographic protocols. In this paper, we introduce a novel concept calledPhysically Related Functions(PReFs) that can exchange secret information in a secure and authenticated manner over insecure networks. This function can be visualized as an abstraction of Strong Physically Unclonable Functions (PUFs). Strong PUFs have the limitation in communicating between two identical devices, an issue that we address in the definition of PReFs. We describe a formal framework for analyzing the functional and security requirements of PReFs. In this framework, we present a lightweight (in terms of computation cost) yet provably secure authenticated key-exchange protocol that relies only on PReFs and makes no additional assumptions (such as secure storage of cryptographic keys). Finally, we present a proof-of-concept realization of PReFs in hardware over Digilent Cora Z7 – a low-cost development platform (consisting of an ARM Cortex processor and a Xilinx FPGA) that is particularly suitable for real-world IoT applications involving resource-constrained devices. We validate that our realization of PReFs satisfies all the properties warranted by our formal framework. We further demonstrate the efficacy of our proposed protocol by analyzing its performance (in terms of computational and communication latency) over the Digilent Cora Z7 platform.

Security is a critical aspect in many of the latest embedded and IoT systems. Malware is one of the severe threats of security for such devices. There have been enormous efforts in malware detection and analysis; however, occurrences of newer varieties of malicious codes prove that it is an extremely difficult problem given the nature of these surreptitious codes. In this article, instead of addressing a general solution, we aim at malware detection for platforms that have more than one core for performance enhancement. We investigate the utility of multiple cores from the point of view of security, where one of the cores operate as a watchdog. We define a notion of a new metric called LAMBDA (Lightweight Assessment of Malware for emBeddeD Architectures), denoted by λ, indicating a conceptual boundary between the programs which are allowed to run on a given platform, with the codes that are suspected as malwares. The metric λ is computed using carefully chosen monitors or features, which are tuples of high-level programs representing OS resources, along with low-level hardware performance counters. In comparison to heavy-weight machine learning techniques, we use an online hypothesis testing, in the form of t -test, to classify a given program-under-test. For applications where security is of prime concern, we propose an additional step based on multivariate analysis to classify the unknown programs that are closer to the threshold with a high degree of confidence. We present experimental results focusing on an ARM-based platform which validate that the proposed approach provides a lightweight, accurate assessment of malware codes for embedded platforms. In addition to it, we also present a security analysis to show the difficulty of a mimicry attack attempting to bypass LAMBDA.

Assessment of the security provided by a fault attack countermeasure is challenging, given that a protected cipher may leak the key if the countermeasure is not designed correctly. This paper proposes, for the first time, a statistical framework to detect information leakage in fault attack countermeasures. Based on the concept of non-interference, we formalize the leakage for fault attacks and provide a t-test based methodology for leakage assessment. One major strength of the proposed framework is that leakage can be detected without the complete knowledge of the countermeasure algorithm, solely by observing the faulty ciphertext distributions. Experimental evaluation over a representative set of countermeasures establishes the efficacy of the proposed methodology.

We present the first practically realizable side-channel assisted fault attack on any block-ciphers having bit-permutation with optimal diffusion, that can retrieve the round key efficiently using random nibble faults. The attack demonstrates how side-channel leakage can allow the adversary to precisely determine the fault mask resulting from a nibble fault injection instance. We first demonstrate the viability of such attack model via side-channel analysis experiments on top of a laser-based fault injection setup, targeting a PRESENT-80 and GIFT-128 (two popular block-ciphers based on bit-permutation having optimal diffusion) implementation on an ATmega328P microcontroller. Subsequently, we present a differential fault analysis (DFA) exploiting the knowledge of the output fault mask in the target round to recover multiple last round keys nibbles independently and in parallel. We show that the combined attack can recover the last round key of PRESENT-80 and GIFT-128 with 4 random nibble fault injections in the best case. In the average case, the number of random nibble faults required for PRESENT-80 and GIFT-128 are 9-18 and 6-9 respectively.

Common Criteria (CC) and FIPS 140-3 are two popular side channel testing methodologies. Test Vector Leakage Assessment Methodology (TVLA), a potential candidate for FIPS, can detect the presence of side-channel information in leakage measurements. However, TVLA results cannot be used to quantify side-channel vulnerability and it is an open problem to derive its relationship with side channel attack success rate (SR), i.e., a common metric for CC. In this paper, we extend the TVLA testing beyond its current scope. Precisely, we derive a concrete relationship between TVLA and signal to noise ratio (SNR). The linking of the two metrics allows direct computation of success rate (SR) from TVLA for given choice of intermediate variable and leakage model and thus unify these popular side channel detection and evaluation metrics. An end-to-end methodology is proposed, which can be easily automated, to derive attack SR starting from TVLA testing. The methodology works under both univariate and multivariate setting and is capable of quantifying any first order leakage. Detailed experiments have been provided using both simulated traces and real traces on SAKURA-GW platform. Additionally, the proposed methodology is benchmarked against previously published attacks on DPA contest v4.0 traces, followed by extension to jitter based countermeasure. The result shows that the proposed methodology provides a quick estimate of SR without performing actual attacks, thus bridging the gap between CC and FIPS.

Characterizing the fault space of a cipher to filter out a set of faults potentially exploitable for fault attacks (FA), is a problem with immense practical value. A quantitative knowledge of the exploitable fault space is desirable in several applications, such as security evaluation, cipher construction and implementation, design, testing of countermeasures, and so on. In this paper, we investigate this problem in the context of block ciphers. The formidable size of the fault space of a block cipher mandates the use of an automation strategy to solve this problem, which should be able to characterize each individual fault instance quickly. On the other hand, the automation strategy is expected to be applicable to most of the block cipher constructions. Existing techniques for automated fault attacks do not satisfy both of these goals simultaneously, and hence are not directly applicable in the context of exploitable fault characterization. In this paper, we present a supervised machine learning assisted automated framework, which successfully addresses both of the criteria mentioned. The key idea is to extrapolate the knowledge of some existing FAs on a cipher to rapidly figure out new attack instances. Experimental validation of this idea on two state-of-the-art block ciphers - PRESENT and LED - establishes that our approach is able to provide fairly good accuracy in identifying exploitable fault instances at a reasonable cost. Utilizing this observation, we propose a statistical framework for exploitable fault space characterization, which can provide an estimate of the success rate of an attacker corresponding to the given fault model and fault location. The framework also returns test vectors leading toward successful attacks. As a potential application, the effect of different S-Boxes on the fault space of a cipher is evaluated utilizing the framework.

Searchable symmetric encryption (SSE) allows a client to outsource the storage of her data to an (untrusted) server in a private manner, while maintaining the ability to selectively search over it. A key feature of all existing SSE schemes is the tradeoff between security (in terms of the information leakage to the server) and efficiency (in terms of the operational and storage overhead on the server and client sides). The premise of this work is that SSE schemes typically offer scope for massively parallel implementations with improved efficiency without compromising security. Based on this idea, we propose a highly scalable framework for parallelized SSE implementations using hardware-based crypto-accelerators, interfaced with a software-based control unit and a memory controller unit. We choose field programmable gate arrays (FPGAs) as the platform for the crypto-accelerators due to their flexibility, reconfigurability, low time-to-market and low maintenance overheads. As a case study, we illustrate how the recently proposed SSE scheme of Lai et al. (CCS'18) may be implemented as per our framework, and the benefits thereof, including shorter preprocessing time and reduced query-response latency as compared to a software implementation.

The widespread advent of Cyber-Physical Systems~(CPS), intertwined with the Internet of Things~(IoT), allows billions of resource-constrained embedded devices to be connected at the same time. While this significantly enhances the scope for productivity, it also throws up security issues which, unless addressed, could lead to catastrophic consequences. The biggest challenge in an IoT network is to ensure inter-device authentication and secure key-exchange, while taking into account the heterogeneous nature of the participating devices in terms of processing capacity and memory bandwidth. In this paper, we propose a secure and operationally asymmetric authenticated key-exchange protocol targeting oT networks and CPS. Our protocol balances security and efficiency, delegates complex cryptographic operations to the resource-equipped servers, and carefully manages the workload on the resource- constrained nodes via the use of unconventional lightweight primitives such as Physically Unclonable Functions (PUFs). The security of our protocol is based on well-established cryptographic assumptions.

The recently proposed Oblivious Cross-Tags (OXT) protocol (CRYPTO 2013) has broken new ground in designing efficient searchable symmetric encryption (SSE) protocol with support for conjunctive keyword search in a single-writer single-reader framework. While the OXT protocol offers high performance by adopting a number of specialised data-structures, it also trades-off security by leaking 'partial' database information to the server. Recent attacks have exploited similar partial information leakage to breach database confidentiality. Consequently, it is an open problem to design SSE protocols that plug such leakages while retaining similar efficiency. In this paper, we propose a new SSE protocol, called Hidden Cross-Tags (HXT), that removes 'Keyword Pair Result Pattern' (KPRP) leakage for conjunctive keyword search. We avoid this leakage by adopting two additional cryptographic primitives - Hidden Vector Encryption (HVE) and probabilistic (Bloom filter) indexing into the HXT protocol. We propose a 'lightweight' HVE scheme that only uses efficient symmetric-key building blocks, and entirely avoids elliptic curve-based operations. At the same time, it affords selective simulation-security against an unbounded number of secret-key queries. Adopting this efficient HVE scheme, the overall practical storage and computational overheads of HXT over OXT are relatively small (no more than 10% for two keywords query, and 21% for six keywords query), while providing a higher level of security.

This paper presents the first practical fault attack on the ChaCha family of addition-rotation-XOR (ARX)-based stream ciphers. ChaCha has recently been deployed for speeding up and strengthening HTTPS connections for Google Chrome on Android devices. In this paper, we propose differential fault analysis attacks on ChaCha without resorting to nonce misuse. We use the instruction skip and instruction replacement fault models, which are popularly mounted on microcontroller-based cryptographic implementations. We corroborate the attack propositions via practical fault injection experiments using a laser-based setup targeting an Atmel AVR 8-bit microcontroller-based implementation of ChaCha. Each of the proposed attacks can be repeated with 100% accuracy in our fault injection setup, and can recover the entire 256 bit secret key using 5-8 fault injections on an average.

We present the first practically realizable sidechannel assisted fault attack on PRESENT, that can retrieve the last round key efficiently using single nibble faults. The attack demonstrates how side-channel leakage can allow the adversary to precisely determine the fault mask resulting from a nibble fault injection instance. We first demonstrate the viability of such an attack model via side-channel analysis experiments on top of a laser-based fault injection setup, targeting a PRESENT-80 implementation on an ATmega328P microcontroller. Subsequently, we present a differential fault analysis (DFA) exploiting the knowledge of the output fault mask in the target round to recover multiple last round key nibbles independently and in parallel. Both analytically and through experimental evidence, we show that the combined attack can recover the last round key of PRESENT with 4 random nibble fault injections in the best case, and around 7- 8 nibble fault injections in the average case. Our attack sheds light on a hitherto unexplored vulnerability of PRESENT and PRESENT-like block ciphers that use bit-permutations instead of maximum distance separable (MDS) layers for diffusion.

Online data sharing for increased productivity and efficiency is one of the primary requirements today for any organization. The advent of cloud computing has pushed the limits of sharing across geographical boundaries, and has enabled a multitude of users to contribute and collaborate on shared data. However, protecting online data is critical to the success of the cloud, which leads to the requirement of efficient and secure cryptographic schemes for the same. Data owners would ideally want to store their data/files online in an encrypted manner, and delegate decryption rights for some of these to users, while retaining the power to revoke access at any point of time. An efficient solution in this regard would be one that allows users to decrypt multiple classes of data using a single key of constant size that can be efficiently broadcast to multiple users. Chu et al. proposed a key aggregate cryptosystem (KAC) in 2014 to address this problem, albeit without formal proofs of security. In this paper, we propose CPA and CCA secure KAC constructions that are efficiently implementable using elliptic curves and are suitable for implementation on cloud based data sharing environments. We lay special focus on how the standalone KAC scheme can be efficiently combined with broadcast encryption to cater to m data users and m' data owners while reducing the reducing the secure channel requirement from O(mm') in the standalone case to O(m + m').

Classical fault attacks, such as differential fault analysis(DFA) as well as biased fault attacks, such as the differential fault intensity analysis (DFIA), have been a major threat to cryptosystems in recent times. DFA uses pairs of fault-free and faulty ciphertexts to recover the secret key. DFIA, on the other hand, combines principles of side-channel analysis and fault attacks to try and extract the key using faulty ciphertexts only. Till date, no effective countermeasure that can thwart both DFA- as well as DFIA-based attacks has been reported in the literature to the best of our knowledge. In particular, traditional redundancy-based countermeasures that assume uniform fault distributions are found to be vulnerable against the DFIA due to its use of biased fault models. In this paper, we propose a novel generic countermeasure strategy that combines the principles of redundancy with that of fault space transformation to achieve security against both DFA- and DFIA-based attacks on AES-like block ciphers. As a case study, we have applied our proposed technique to obtain temporal and spatial redundancy-based countermeasures for AES-128, and have evaluated their security against both DFA and DFIA via practical experiments on a SASEBO-GII board. Results show that our proposed countermeasure makes it practically infeasible to obtain a single instance of successful fault injection, even in the presence of biased fault models.

Many Internet of Things (IoT) applications can potentially benefit from the remote Dynamic Partial Reconfiguration (DPR) capabilities of modern Field Programmable Gate Arrays (FPGAs). Such capabilities enable changes in the circuit mapped on the FPGA, for modification or enhancement of functionality offered by the FPGA without taking it offline, via remote communications over a network. However, the use of remote DPR can result in security threats with catastrophic consequences. In this paper, we design two Hardware Trojan Horse attacks that exploit the remote DPR capability of the FPGA, on an encryption circuit and a true random number generator circuit, respectively. In particular, these attacks target the clock signal management circuitry on the FPGA to disrupt functionality. We substantiate the threat by demonstrating successful remote attacks via transfer of malicious bitstreams to a Virtex-5 FPGA, thereby embedding the HTH. Finally, we propose plausible countermeasures to prevent such attacks.

Recent literature has demonstrated that the security of Physically Unclonable Function (PUF) circuits might be adversely affected by the introduction of faults. In this paper, we propose novel and efficient architectures for a variety of widely used delay-based PUFs which are robust against high precision laser fault attacks proposed by Tajik et al. in FDTC-2015. The proposed architectures can be used to detect run-time modifications in the PUF design due to fault injection. In addition, we propose fault recovery techniques based on either logical reconfiguration or dynamic partial reconfiguration of the PUF design. We validate the robustness of our proposed fault tolerant delay-based PUF designs on Xilinx Artix-7 FPGA platform.

Side-channel attacks are a potent threat to the security of devices implementing cryptographic algorithms. Designing lightweight countermeasures against side-channel analysis that can run on resource constrained devices is a major challenge. One such lightweight countermeasure is shuffling, in which the designer randomly permutes the order of execution of potentially vulnerable operations. State of the art shuffling countermeasures advocate shuffling a set of independent operations in a single round of a cryptographic algorithm, but are often found to be insufficient as standalone countermeasures. In this paper, we propose a two-round version of the shuffling countermeasure, and test its security when applied to a serialized implementation of AES-128 using Test Vector Leakage Assessment (TVLA). Our results show that the required number of traces to break AES-128 implemented using our proposed countermeasure is significantly larger than the implementations using simple one-round shuffling. Furthermore, the new shuffling method has significantly lower overhead of around 1.3 times, as compared to other side-channel countermeasures such as masking that have an overhead of approximately two times.

The availability of an overwhelmingly large amount of bibliographic information including citation and co-authorship data makes it imperative to have a systematic approach that will enable an author to organize her own personal academic network profitably. An effective method could be to have one's co-authorship network arranged into a set of ``circles'', which has been a recent practice for organizing relationships (e.g., friendship) in many online social networks. In this paper, we propose an unsupervised approach to automatically detect circles in an ego network such that each circle represents a densely knit community of researchers. Our model is an unsupervised method which combines a variety of node features and node similarity measures. The model is built from a rich co-authorship network data of more than 8 hundred thousand authors. In the first level of evaluation, our model achieves 13.33% improvement in terms of overlapping modularity compared to the best among four state-of-the-art community detection methods. Further, we conduct a task-based evaluation -- two basic frameworks for collaboration prediction are considered with the circle information (obtained from our model) included in the feature set. Experimental results show that including the circle information detected by our model improves the prediction performance by 9.87% and 15.25% on average in terms of AUC (Area under the ROC) and [email protected] (Precision at Top 20) respectively compared to the case, where the circle information is not present.