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In this section, we describe in detail the mathematical formulation of the 3 dBPP variant tackled in this research. First, input parameters and variables that compose the problem are shown inTable1.

The 3 dBPP can be solved as an optimization problem where a suitable cost function to minimize must be defined. In our case, this cost function is represented as the sum of three objectives. The strength given to each objective, i.e. the relevance accounted for each one, is up to the user preferences just by multiplying each objective with a suitable weight. Thus, the problem can be stated as (min text { }sum _{i=1}^3omega _io_i) with (omega _i) the weights of each objective (o_i). In our study, we will not consider this bias, i.e. (omega _i=1text { }forall i).

The first and main objective minimizes the total amount of bins used to locate the packages. This can be achieved by minimizing

$$begin{aligned} o_1 = sum _{j=1}^nv_j. end{aligned}$$

(1)

Additionally, for ensuring that items are packed from the floor to the top of the bin, avoiding solutions with floating packages, a second objective is defined by minimizing the average height of the items for all bins

$$begin{aligned} o_2 = frac{1}{mH}sum _{i=1}^mleft( z_i + z'_iright) . end{aligned}$$

(2)

Besides these two objectives reformulated from the reference code28, we further add a third optional objective (o_3) to take into account the load balancing feature. This concern is particularly important when air cargo planes and sailings are the chosen conveyance30,31, for example. In those situations, packages should be uniformly distributed around a given xy-coordinate inside the bin. We can tackle this by computing the so-called taxicab or Manhattan distance between items and the desired center of mass for each bin. As a result, the gaps between items are also reduced. Concerning this, the third objective to be minimized is

$$begin{aligned} o_3 = frac{1}{m}left( frac{1}{L}sum _{i=1}^m {tilde{x}}_i + frac{1}{W}sum _{i=1}^m {tilde{y}}_iright) , end{aligned}$$

(3)

with

$$begin{aligned} {tilde{x}}_i {:}{=}left| left( x_i + frac{x_i'}{2}right) !text { mod } L -{tilde{L}} right| quad text {and}quad {tilde{y}}_i {:}{=}left| y_i + frac{y_i'}{2} -{tilde{W}} right| quad forall iin I, end{aligned}$$

(4)

where (0le x_i< nL) (bins stacked horizontally) and (0le y_i< W) (forall iin I). This objective term minimizes for each item the distance between the center of mass projection in the xy-plane and the (({tilde{L}},{tilde{W}})) coordinate of each bin.

The objectives above defined are subject to certain restrictions, which are essential to derive realistic solutions. The whole pool of constraints is separated into two categories: the ones intrinsic to the BPP definition (intrinsic restrictions), and the ones relevant from a real-world perspective (real-world BPP restrictions).

Item orientations: the fact that inside a bin each item must have only one orientation can be implemented by using

$$begin{aligned} sum _{kin K_i}r_{i,k}=1quad forall iin I. end{aligned}$$

(5)

Set of possible orientations (kin K_i) for a given item i of dimensions ((l_i,w_i,h_i)). (a) (k = 1), (b) (k = 2), (c) (k = 3), (d) (k = 4), (e) (k = 5), (f) (k = 6). SeeTable2.

Orientations give rise to the effective length, width, and height of the items along x, y and z axes

$$begin{aligned} x'_i&= l_ir_{i,1} + l_ir_{i,2} + w_ir_{i,3} + w_ir_{i,4} + h_ir_{i,5} + h_ir_{i,6} quad forall iin I, end{aligned}$$

(6)

$$begin{aligned} y'_i&= w_ir_{i,1} + h_ir_{i,2} + l_ir_{i,3} + h_ir_{i,4} + l_ir_{i,5} + w_ir_{i,6} quad forall iin I, end{aligned}$$

(7)

$$begin{aligned} z'_i&= h_ir_{i,1} + w_ir_{i,2} + h_ir_{i,3} + l_ir_{i,4} + w_ir_{i,5} + l_ir_{i,6} quad forall iin I, end{aligned}$$

(8)

and because of (5), only one term (r_{i,k}) is nonzero in each equation.

It should be deemed that there could be items with geometrical symmetries, as with cubic ones where rotations do not apply. Redundant and non-redundant orientations are considered in the reference code28. In our formulation, we previously check if these symmetries exist to define (K_i) for each item. Thanks to this, (6)(8) are simplified filtering out redundant orientations and leading to a formulation which uses less variables (thus qubits) to represent the same problem, where (kappa =sum _{i=1}^m|K_i|le 6m) variables (r_{i,k}) are needed. For (iin I_text {c}) with (I_text {c}{:}{=}{iin I,|,l_i=w_i=h_i}) (cubic items), we can set (r_{i,1}=1) and 0 otherwise, thus satisfying(5) in advance. InTable2, we can see the non-redundant orientation sets for an item i depending on its dimensions. This simple mechanism reduces the complexity of the problem, being favourable for the quantum hardware to implement.

Non-overlapping restrictions: since we are considering rigid packages, i.e. they can not overlap, a set of restrictions need to be defined to overcome these configurations. For this purpose, at least one of these situations must occur (seeFig.2)

$$begin{aligned} text {Item }itext { is at the left of item }k, (q=1)text {:}{} & {} -(2 - u_{i,j}u_{k,j}-b_{i,k,1})nL+x_i+x'_i-x_kle 0{} & {} forall i,kin I,text { }forall jin J, end{aligned}$$

(9)

$$begin{aligned} text {Item }itext { is behind item }k, (q=2)text {:}{} & {} -(2 - u_{i,j}u_{k,j}-b_{i,k,2})W+y_i+y'_i-y_kle 0{} & {} forall i,kin I,text { }forall jin J, end{aligned}$$

(10)

$$begin{aligned} text {Item }itext { is below item }k, (q=3)text {:}{} & {} -(2 - u_{i,j}u_{k,j}-b_{i,k,3})H+z_i+z'_i-z_kle 0{} & {} forall i,kin I,text { }forall jin J, end{aligned}$$

(11)

$$begin{aligned} text {Item }itext { is at the right of item }k, (q=4)text {:}{} & {} -(2 - u_{i,j}u_{k,j}-b_{i,k,4})nL+x_k+x'_k-x_ile 0{} & {} forall i,kin I,text { }forall jin J,end{aligned}$$

(12)

$$begin{aligned} text {Item }itext { is in front of item }k, (q=5)text {:}{} & {} -(2 - u_{i,j}u_{k,j}-b_{i,k,5})W+y_k+y'_k-y_ile 0{} & {} forall i,kin I,text { }forall jin J, end{aligned}$$

(13)

$$begin{aligned} text {Item }itext { is above item }k, (q=6)text {:}{} & {} -(2 - u_{i,j}u_{k,j}-b_{i,k,6})H+z_k+z'_k-z_ile 0{} & {} forall i,kin I,text { }forall jin J. end{aligned}$$

(14)

As discussed with the orientation variable (r_{i,k}) in(5), the relative position between items i and k must be unique, so

$$begin{aligned} sum _{qin Q}b_{i,k,q}=1quad forall i,kin I. end{aligned}$$

(15)

Representation of (b_{i,k,q}) activated for all relative positions (qin Q) between items i and k. See(9)(14). Both are in contact but it is not mandatory. (a) (b_{{i},{k},1}=1), (b) (b_{{i},{k},2}=1), (c) (b_{{i},{k},3}=1), (d) (b_{{i},{k},4}=1), (e) (b_{{i},{k},5}=1), (f) (b_{{i},{k},6}=1).

Item and container allocation restrictions: the following set of restrictions guarantees an appropriate behaviour during item and bin assignment. In order to avoid packing duplicates of the same item, each item must go to exactly one bin, where

$$begin{aligned} sum _{j=1}^n u_{i,j}=1quad forall iin I. end{aligned}$$

(16)

The following formula verifies if items are being packed inside bins that are already in use

$$begin{aligned} sum _{i=1}^m(1-v_j)u_{i,j}le 0quad forall jin J, end{aligned}$$

(17)

so it activates (v_j) if needed during packaging. Bins can be activated sequentially to avoid duplicated solutions ensuring that

$$begin{aligned} v_jge v_{j+1}quad forall jin Jtext { } | text { }jne n. end{aligned}$$

(18)

Bin boundary constraints: in order to contemplate bin boundaries, the following set of restrictions must be met

$$begin{aligned}{} & {} x_i+x'_i-jL le (1-u_{i,j})nL quad forall iin I,text { }forall jin J, end{aligned}$$

(19)

$$begin{aligned}{} & {} x_i-(j-1)Lu_{i,j} ge 0 quad forall iin I,text { }forall jin Jtext { }|text { }j>1, end{aligned}$$

(20)

$$begin{aligned}{} & {} y_i+y'_i-W le (1-u_{i,j})W quad forall iin I,text { }forall jin J, end{aligned}$$

(21)

$$begin{aligned}{} & {} z_i+z'_i-H le (1-u_{i,j})H quad forall iin I,text { }forall jin J, end{aligned}$$

(22)

where(19) guarantees that items i placed inside the bin j are not outside of the last bin (n-th bin) along the x axis, (20) ensures that item i is located inside of its corresponding bin j along the x axis (activated if (n>1)), (21) confirms that item i placed inside the bin j is not outside along the y axis, while(22) ensures that item i allocated inside the bin j is not outside along the z axis.

In this subsection we introduce those restrictions related with the operative perspective of the problem, i.e. the ones that consider real-world industrial situations. All of the following constraints are optional in our formulation.

Overweight restriction: the weight of each package and the maximum capacity of containers are common contextual data to avoid exceeding the maximum weight capacity of bins, so avoid overloaded containers. We can introduce this restriction as

$$begin{aligned} sum _{i=1}^mmu _iu_{i,j}le Mquad forall jin J. end{aligned}$$

(23)

This restriction is activated if the maximum capacity M is given.

Affinities among package categories: there are commonly preferences for separating some packages into different bins (negative affinities or incompatibilities) or, on the contrary, gathering them into the same container (positive affinities). Let us consider (I_alpha {:}{=}{iin Itext { }|text { }{} texttt {id}text { of }itext { is equal to }alpha }), i.e. (I_alpha subset I) is a subset of all items labelled with id equal to (alpha). Given a set of p negative affinities (A^text {neg}{:}{=}{(alpha _1,beta _1),dots ,(alpha _p,beta _p)}), then the restriction will be

$$begin{aligned} sum _{(alpha ,beta )in A^text {neg}},sum _{(i_alpha ,i_beta )in I_alpha times I_beta },sum _{j=1}^nu_{i_alpha ,j}u_{i_beta ,j}=0, end{aligned}$$

(24)

To activate this restriction, a set of incompatibilities must be given. Moreover, we can satisfy in advance (nu {:}{=}6nsum _{(alpha ,beta )in A^text {neg}}|I_alpha ||I_beta |) non-overlapping constraints (see(9)(14)), leading to a simpler formulation. Conversely, given a set of positive affinities (A^text {pos}) as stated with (A^text {neg}), then the restriction will be posed such that

$$begin{aligned} sum _{(alpha ,beta )in A^text {pos}},sum _{(i_alpha ,i_beta )in I_alpha times I_beta },sum _{j=1}^nleft( 1-u_{i_alpha ,j}u_{i_beta ,j}right) =0, end{aligned}$$

(25)

This restriction is activated if a set of positive affinities is given. If (A^text {pos}) and (A^text {neg}) are given, then both restrictions can be introduced using just one formula adding(24) and (25).

Preferences in relative positioning: relative positioning of items demands that some of them must be placed in a specific position with respect other existing items. This preference allows introducing the ordering of a set of packages according to their positions with respect to the axes. Thus, this preference assists in ordering for many real cases such as: parcel delivery (an item i that has to be delivered before item k will be preferably placed closer to the trunk door) or load bearing (no heavy package should rest over flimsy packages), among others.

Regarding this preference, we can define two different perspectives to treat relative positioning:

Positioning to avoid ((P_q^{-})): list of items (i,k) should not be in the relative position (qin Q) specified. So, (b_{i,k,q}=0) is expected, favouring configurations where the solver selects (q'in Q) with (q'ne q) for the relative positioning of items (i,k).

Positioning to favour ((P_q^{+})): list of items (i,k) should be in a certain relative position q. Activated this preference, (b_{i,k,q}=1) ought to hold and consequently, (b_{i,k,q'}=0 forall q'ne q).

Formally, these preferences are written as

$$begin{aligned} P_q^{-}{:}{=}{(i,k)in I^2text { }|text { }i

(26)

These preferences could be also treated as compulsory pre-selections. In such case, the number of variables needed would be reduced, so would the search space. If we let (smash [t]{p^{-}=sum _{qin Q}|P_q^{-}|}) and (smash [t]{p^{+}=sum _{qin Q}|P_q^{+}|}) with (smash [t]{P^{-}_qcap P^{+}_{q'}=varnothing }), based on(15), the amount of variables reduced would be given by (smash [t]{p^{-}+6p^{+}}). Moreover, (smash [t]{n(p^{-}+5p^{+})}) non-overlapping constraints (see(9)(14)) are satisfied directly and can be ignored, thus simplifying the problem. In this paper, for the sake of clarity, these preferences have been applied for load bearing purposes as hard constraints (HC), as explained in the upcoming Experimental results.

Load balancing: to activate this restriction, a target center of mass must be given. Global positions with respect to the bin as a whole (as described in objective (o_3) in(3)), are fixed using the following constraints

$$begin{aligned} pm frac{1}{n}sum _{j=1}^nleft[ x_i+frac{x_i'}{2} - n(j-1)u_{i,j}L -{tilde{L}}right] le {tilde{x}}_i quad text {and}quad pm left( y_i+frac{y_i'}{2} -{tilde{W}}right) le {tilde{y}}_i quad forall iin I. end{aligned}$$

(27)

This feature is represented inFig.3 for (({tilde{L}},{tilde{W}})=(L/2,W/2)), whose red line shows the available ({tilde{x}}_i) and ({tilde{y}}_i) values (see(4)).

Representation of available ({tilde{x}}_i) and ({tilde{y}}_i) values ensured by the constraints given in(27) for (({tilde{L}},{tilde{W}}) = (L/2,W/2)).

Regarding the complexity of the 3 dBPP proposed in this research, the total amount of variables needed to tackle an arbitrary instance is given inTable3, where our formulation scales as ({mathscr {O}}[m^2+nm]) in terms of variables. Additionally, the total amount of constraints required is provided inTable4, whose quantity grows quadratically as ({mathscr {O}}[m^2+nm]).

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Hybrid approach for solving real-world bin packing problem ... - Nature.com

As a CISO and cyber specialist, I am often asked what I see as the big cyber threats of the future. Whilst Im not a fan of crystal ball gazing for its own sake, nevertheless it can be helpful to think about what may be coming and what we can do about it.

So here are my four big threats or what we may more colourfully term the four horsemen of the apocalypse together with some thoughts how we can prepare for them so that it doesnt actually turn into the end of the world!

With the advent of AI, especially natural language algorithms like ChatGPT, and their access to everything on the internet, combined with the ability to create essentially AI plug-ins for text-to-speech and imagery, very soon well have more virtual humans online than real ones.

Today we have botnets: networks of robots that were surreptitiously installed through malware onto computing systems around the world doing the bidding of cybercriminals. With the power of millions of computers at their disposal, industrious hackers can do everything from mine crypto to offer ransomware as a service to other criminals.

Moving forward, cybercriminals and even nation states will have the ability to mobilize huge swaths of digital people seemingly operating independently but aligned with a larger mission. We see tiny examples of this today with virtual interviews resulting in unintentionally hiring a hacker or spy.

Real humans are and will remain victims to fraud and confidence schemes. Even to this day, email borne attacks, such as phishing, are highly effective. Imagine a world where parents are having interactive video calls with their children asking for money. But what if that child is actually a digital fake? Given how much information there is about you as an individual, thanks to data breaches and social media posts, very rapidly there will emerge virtual replicas. Versions of you designed to leverage you for a greater gain by crossing ethical boundaries you are not willing to take.

Quantum computing has leapt off the pages of sci-fi into reality and has been actively processing data not just for a few years now, but decades. Many companies have developed quantum computers, but the reason we have yet to see something dramatic is, in many ways, because they all use a different architecture. Its like Apple and Microsoft in 1986, separate and completely incompatible. Moreover, thanks to the nuances of quantum mechanics, networking quantum computers has proven to be difficult.

Nevertheless, both these barriers are diminishing rapidly. Soon the race for processing the most qubits will be shortened and accelerated as scientists solve the networking challenge. Overnight, the global human race will have access to thousands if not tens of thousands of qubits.

From a cybersecurity perspective, most encryption will instantly be rendered useless. All of a sudden, your secure transaction to your bank or all the data transmitted over you VPN are no longer protected. In fact, every secure interaction youve ever made is likely to have been collected, allowing adversaries to go back and decrypt all those communications. The underlying basis of blockchain crumbles, permitting the ability to rewrite financial history.

As we delve into the world of digital transformation and Web 3.0, the ecosystem of technology is becoming increasingly complex and layered. In the early days computers existed in a single room. Soon, individual computers were able to communicate. As networks expanded, along with processing speeds and availability of cheap storage, computer applications began to interact, requiring less and less standardization across platforms. With this evolution has come more points of interaction and the ability to leverage specific capabilities from a wider range of technologies, and at different layers of computing.

Today, cybersecurity is just coming to grips with the challenges of third-party and supply chain risk in computing. Companies that are currently undergoing digital transformation will likely not simply have three or four layers of suppliers, but that rather closer to twenty.

Moving forward the combined demand for pace, growth and innovation will require more and more from the computing ecosystem. These pressures will result in greater degrees of specialization in the supply chain causing it to expand rapidly. As such, it will be a primary target of cybercriminals because its manipulation can undermine trust in surface-level computing, permitting hackers to take control of any system without detection.

The role of technology and its importance in the physical world is increasing exponentially and will soon reach a point where computer-related issues, including everything from errors to hackers, will have a tangible impact in the real world.

Today, were exploring self-driving vehicles, intelligent power distribution, and automation in industrial control systems, all of which have direct physical interactions with people and places.

As we evolve, increasingly sophisticated technology will not only be embedded into everything from the mundane toaster to the most complex infrastructure but will also be interconnected and operated across a set of automated systems. For example, smart medical devices will become increasingly common and will quickly move beyond tactical monitoring to automated delivery of off the shelf medication, prioritization of emergency services, and even control access to various facilities.

While these capabilities will greatly enhance human services, improve healthcare, and reduce accidents, cyberthreats will target these systems to perform everything from theft to terrorism. Instead of your data being held ransom, hackers may hold your car for ransom, withhold access to your home for money, or deny you medication or emergency services without payment.

In the face of these seemingly insurmountable challenges, is there any light at the end of the tunnel? Thankfully, I believe there is.

For example, many companies are now developing quantum-resistant technologies, such as encryption algorithms, blockchain technology, and communication networks. These may help nullify some of the cyber risks of quantum computing the challenge will be to develop the strength of the defenses in proportion to the magnitude of the risks as quantum computing takes off.

In relation to the expanding ecosystem, although the supply chain is growing beyond comprehension, there are efforts such as Software Bill of Materials (SBOM), enhanced software updating and patching standards, and even IoT product labeling is being explored. Active expert thinking is being applied to the issue.

When dealing with the future related to smart devices and now, with ChatGPT and its ilk, smart AI, I think we have to change our perspective of how we coexist as companies and individuals with technology. Its less about being a hard target with strong defenses, and rapidly becoming all about being a resilient target rather than a victim. With solid planning and preparation, resilience is possible. Be aware of the risks and think ahead of them. Focus on having alternatives, out-of-band options, and, critically, awareness of potential threat capabilities so that your plan B and even plan C arent rendered useless.

The cyber future may sound worrying but at the same time, human ingenuity will also find ways to build new protections and mitigations.

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Future Cyber Threats: The four horsemen of the apocalypse - ComputerWeekly.com

Generative AI has been heralded as the most profoundly impactful technological innovation since the iPhone. ChatGPT in particular has captured the worlds attention with its ability to convincingly generate whatever text the user desires. It has passed numerous standardized tests, from the Bar exam to the SATs, and its essay-writing prowess poses an existential threat to the integrity of education itself.

Other tools have shown impressive results in generating art, videos, code, and more. Not surprisingly, many predict that these generative AI tools will be widely disruptive particularly for industries like media, marketing and legal that deal with text and images. Whats less obvious is how generative AI will impact supply chains.

The truth is that text and image generation is just the beginning of what generative AI can accomplish. It can also be used to generate solutions to optimization problems that abound within supply chains.

Generally speaking, any situation where you have a wide range of possible solutions, and you want to find the best solution, can be thought of as an optimization problem. A simple example is when Google Maps tries to find the fastest possible route to your destination. While this works reliably well, for more complex optimization problems, classical computers dont have an efficient way to find the best solution and can only generate approximate solutions.

In contrast, a generative model could be trained on the best existing solutions to an optimization problem for example those obtained from classical heuristics or MIP solvers and learn what makes a good solution good. Much like how ChatGPT learns from existing text to generate new text, this generative model could then generate new solutions to the optimization problem. We call this approach Generator-Enhanced Optimization (GEO).

Potential supply chain use cases include finding more efficient shipping routes, optimizing the organization of warehouses to speed up order-picking, or selecting the best combination of suppliers, distributors and vendors. Given the complexity of most global supply chains, there is ample room for optimization and cost savings as a result.

It sounds promising, but for years quantum computing has also been touted for its ability to solve optimization problems, and yet today there is not yet a documented example of quantum computers providing an advantage for optimization. However, generative AI may be the fastest avenue to realize that quantum advantage. It may also be the first place we see a practical quantum advantage at all.

To vastly oversimplify things, generative models like those behind ChatGPT work by learning patterns in massive datasets and producing new data that conforms to these patterns. In other words, they learn to replicate the probability distributions of the training data. Quantum computers have the ability to encode and sample from complex probability distributions in a way that classical computers cannot, giving them a potential advantage in generative modeling.

How is this possible? For one, quantum entanglement can encode distant correlations within a dataset in ways that would be difficult for a classical computer to simulate. Secondly, the inherently probabilistic nature of measuring a quantum state makes quantum computers the ideal vehicle for sampling from probability distributions.

The end result is the ability to generate a more diverse range of solutions to the generative modeling task. In the context of optimization, this means quantum generative models could generate new, previously unconsidered solutions.

But theres a catch. Quantum devices are currently limited by low qubit counts and high error rates. But we dont necessarily need quantum devices. However, tensor networks, originally popularized among quantum physicists for simulating quantum states on classical computers, can be used for generative modeling today. And as quantum hardware matures, these quantum-inspired models can be translated into real quantum circuits, making them forward compatible with future quantum devices.

Tensor networks have shown particular value for optimization problems with equality constraints. An equality constraint is a condition that must be satisfied exactly for the solution to be valid. Without a way to natively encode these constraints, traditional optimizers can generate many invalid solutions, resulting in inefficient and expensive searches.

On the other hand, tensor networks can be constrained in a way that only outputs valid samples, resulting in the generation of more novel and high-quality solutions to optimization problems. And while equality constraints can worsen the performance of other quantum or quantum-inspired approaches, the opposite is true with constrained tensor networks, which deliver better computational performance at a cheaper cost for each additional equality constraint.

There are many possible applications of GEO that could make the supply chain more efficient. Below are a few examples:

Of course, supply chains can vary widely from industry to industry. You may have additional optimization use cases that are unique to your business. But across the board, generating better optimization solutions has the potential to reduce costs and speed up the supply chain. Optimization could also reduce waste and cut carbon emissions a great place to start for businesses looking for ways to reduce their carbon footprint.

How great is the potential value at stake? The only way to find out is to try. We are still in the early days of generative AI and even more so with quantum-inspired generative AI. By building and deploying generative AI applications, not only do you stand to gain a competitive advantage, but you may also make discoveries that advance the field.

Its important to reiterate that tensor networks are forward-compatible with real quantum computation. Businesses that deploy tensor networks for optimization may not only gain an advantage today, but they would also be in position to gain a potentially greater advantage as quantum hardware becomes more powerful. In other words, they will become quantum ready.

Excerpt from:
How Quantum-Enhanced Generative AI Could Help Optimize ... - Supply and Demand Chain Executive

The Israel Innovation Authority announced on Thursday the launch of a NIS 36 million (approximately $10 million) fund for human capital programs aimed at fostering expertise in quantum, AI, cleantech, foodtech, and Bio-Convergence. Through the joint governmental and private sector investment, approximately 2,000 participants will take part in 20 programs over the next two years.

The call for proposals, based on the Authority's program known as "Human Capital Fund for High-Tech", was launched in March 2023 to establish specialized human capital by offering training and qualifications in advanced technological fields. The Israel Innovation Authority, in response to the recommendations of the National Infrastructure Forum for Research and Development (Telem), will allocate a significant portion of approximately NIS 5 million ($1.4 million) to enable in-depth training for the integration of skilled professionals into the quantum computing industry.

Additionally, two pilot programs, developed in collaboration with the Ministry of Defense, have been approved to train and place Druze community members in crucial roles in artificial intelligence, AgriTech, and water technology.

From 49 program proposals received, 20 were selected to operate across the country. These programs will receive a cumulative government grant of NIS 19.8 million ($5.5 million) to facilitate the training and placement of 1,920 graduates from diverse populations.

Organizations selected to run programs include The Hebrew University, Elevation Education, The Technion, Master School, Science Abroad, Brainstorm IL, 8200 Alumni Association, AYYT Technological Applications and Data, Ramot at Tel Aviv University, Rambam Medical Center, Jordan Valley Academic College, Epsilon Climate, RACHIP VERIFICATION LTD, IDC Tech, Extra-tech, StellarAI, and Infinity Labs.

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Israel Innovation Authority to invest $10 million to develop human ... - CTech

From the shocking scoop of Matt Hancocks Whatsapp messages to Boris reticence to share his messages, the use of secure messaging has become a hot topic. Although it may seem like a new trend, the recent stream of government-centred controversies are far from the first weve seen, nor are they the most costly. Last year alone, US financial institutions were fined almost two billion dollars for communicating over messaging services like WhatsApp.

Instant messaging is now a preferred means of communication, it is convenient, easily digestible and seen as secure. Whether we like it or not, the simple truth is that messaging is here to stay. We must accept this and work to update the way we view and manage messaging so as to ensure security and regulatory compliance.

As a former computer auditor for major financial institutions, turned cybersecurity expert, who now runs a company that provides secure messaging services, I can say from experience that there are three issues which businesses and governments are failing to take action on. The first I call the compliance problem, the second is the metadata problem and the third is the quantum problem. Unless we can address all three issues, we are sleepwalking into an oblivion of our own making.

As I previously mentioned, late last year major financial institutions in the US were fined almost two billion dollars for failing to prevent their employees from using non-compliant messaging services. Furthermore, there has been significant public debate over the so-called government by WhatsApp over the pandemic in the UK.

Historically, communications in finance and government were meticulously recorded and took place through official channels which had official norms. This is because, in the case of both of these areas, significant oversight is required in order to ward off malpractice and provide accountability. Many secure messaging services prevent the monitoring of communications, which is key to providing this essential compliance.

This is because with many secure messaging services the central institutions (e.g., watchdogs, governments, etc.) have no control over the messages and whether they are deleted. It also prevents regulators from accessing them in some cases. This is why banking regulators are coming down so hard when institutions use tools without an audit trail.

Fines and controversy can easily be avoided by implementing a secure chat solution without weakening any encryption, but also one that permits access to regulators in the event an investigation is required.

On average, financial institutions and governments take data security incredibly seriously. As a norm VPNs are required to access data and messaging services are centrally controlled and very secure. In finance in particular, even small data leaks can have huge implications in terms of financial and reputational losses.

For instance, imagine if metadata were obtained which showed that a CEO from a major company was messaging a CEO from another major company. Even if the content of the messages could not be seen, it may lead to inferences that an acquisition could soon take place. The speculation could have a huge impact on markets.

Many users of generic messaging platforms do not realise that this metadata can be accessed, compiled, analysed and sold on. Secure messaging providers can often access metadata like this even if end-to-end encryption prevents them from seeing the content of the message itself. Therefore, the use of these third-party messaging services can risk this metadata falling into the wrong hands and exposing all manner of sensitivities.

Thats why security and privacy sensitive industries need their own proprietary messaging services, where they control the infrastructure as well as encryption and the metadata insights that are inevitably emitted.

When a sufficiently large quantum computer is developed it will be able to access almost all encrypted data. While this may still be a few years off, encrypted data can be harvested now so that it can be decrypted later when a large enough quantum computer is developed.

Governments and major financial institutions routinely handle data which will remain sensitive for decades. This data is vulnerable to Harvest Now, Decrypt Later attacks even if it is sent over messaging services like WhatsApp that rely only on todays encryption algorithms. To be truly secure today, a messaging service needs to encrypt against both classical and quantum attacks. The latest US legislation makes this point abundantly clear by mandating federal agencies transition to post-quantum encryption.

So, what should we do?

Now that messaging is such a core part of how we communicate, we need to start a dialogue on how to do it right. At a bare minimum, we must implement solutions which are regulatorily compliant and are secure from metadata mining and quantum attack.

Organisations have long understood these principles as they relate to general networking but now is the time to apply those same tried and tested principles to how we chat.

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The problem with secure messaging - ComputerWeekly.com